diff --git a/src/ai_analysis/genre_prediction.ipynb b/src/ai_analysis/genre_prediction.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read processed song data from pkl file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded 100391 processed tracks\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "results_file = 'audio_features.pkl'\n",
+    "\n",
+    "if os.path.exists(results_file):\n",
+    "    with open(results_file, 'rb') as file:\n",
+    "        saved_data = pickle.load(file)\n",
+    "    X = saved_data.get('X', [])\n",
+    "    y_labels = saved_data.get('y_labels', [])\n",
+    "    \n",
+    "    processed_files = set(saved_data.get('processed_files', []))\n",
+    "    print(f\"Loaded {len(processed_files)} processed tracks\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preprocess"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Turn X into an np array for easier processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape of feature matrix X:  (100391, 55)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "X = np.array(X)\n",
+    "print(\"shape of feature matrix X: \", X.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Remove all entries where the genre is defined as None and relabel hip-hop as hip hop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "X = X[np.array(y_labels) != None]\n",
+    "y_labels = np.array(y_labels)[np.array(y_labels) != None]\n",
+    "\n",
+    "genre_mapping = {\n",
+    "    'hip-hop': 'hip hop'\n",
+    "}\n",
+    "\n",
+    "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Show label fequency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "y_labels_normalized_df = pd.DataFrame(y_labels_normalized)\n",
+    "\n",
+    "# Count the frequency of each label in the \"label\" column\n",
+    "label_counts = y_labels_normalized_df.value_counts().reset_index()\n",
+    "\n",
+    "# Rename the columns for clarity\n",
+    "label_counts.columns = ['Label', 'Frequency']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Remap genres"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "popular_genres = {\n",
+    "    'country', 'metal', 'heavy-metal' 'hardstyle',\n",
+    "    'blues', 'rap', 'hip hop', 'classical', 'folk',\n",
+    "    'jazz', 'lo-fi', 'soul', 'punk', 'punk-rock', 'drill',\n",
+    "    'rock', 'rock-n-roll', 'techno', 'pop', 'house', 'phonk', 'r&b',\n",
+    "    'indie', 'trap', 'idm', 'reggae', 'funk', 'acid',\n",
+    "    'alt-rock', 'death-metal'\n",
+    "}\n",
+    "\n",
+    "# country, metal, hardstyle, heavy-metal\n",
+    "# blues, rap, hip hop, classical, folk\n",
+    "# jazz, lo-fi, soul, punk, punk-rock, drill\n",
+    "# rock, techno, pop, house, phonk, r&b\n",
+    "# indie, trap, electronic, idm, reggae, funk\n",
+    "# acid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Replace sub-genres with their parent genre based "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "\n",
+    "genre_patterns = [(genre, re.compile(rf'\\b{re.escape(genre)}\\b', re.IGNORECASE)) for genre in popular_genres]\n",
+    "\n",
+    "def simplify_labels(y_labels):\n",
+    "    new_labels = []\n",
+    "    for label in y_labels:\n",
+    "        matched = False\n",
+    "        for genre, pattern in genre_patterns:\n",
+    "            if pattern.search(label):\n",
+    "                new_labels.append(genre)\n",
+    "                matched = True\n",
+    "                break\n",
+    "        if not matched:\n",
+    "            new_labels.append(label)  # Keep original if no match\n",
+    "    return new_labels\n",
+    "\n",
+    "y_labels_normalized = simplify_labels(y_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "genre_mapping = {\n",
+    "    'lo-fi beats': 'lo-fi',\n",
+    "    'chillwave': 'lo-fi',\n",
+    "\n",
+    "    'nu metal': 'metal',\n",
+    "    'folk metal': 'metal',\n",
+    "    'heavy metal': 'metal',\n",
+    "    'metalcore': 'metal',\n",
+    "    'power metal': 'metal',\n",
+    "    'industrial metal': 'metal',\n",
+    "    'glam metal': 'metal',\n",
+    "    'melodic death metal': 'metal',\n",
+    "    'progressive metal': 'metal',\n",
+    "    'thrash metal': 'metal',\n",
+    "    'gothic metal': 'metal',\n",
+    "    'groove metal': 'metal',\n",
+    "    'death metal': 'metal',\n",
+    "    'alternative metal': 'metal',\n",
+    "    'black metal': 'metal',\n",
+    "    'speed metal': 'metal',\n",
+    "\n",
+    "    'acid house': 'acid',\n",
+    "    'acid techno': 'acid',\n",
+    "\n",
+    "    'afrobeat': 'afro',\n",
+    "    'afropop': 'afro',\n",
+    "\n",
+    "    'art rock': 'rock',\n",
+    "\n",
+    "    'britpop': 'alternative rock',\n",
+    "    \n",
+    "    'art pop': 'pop',\n",
+    "    'acoustic pop': 'pop',\n",
+    "    'bedroom pop': 'pop',\n",
+    "    'soft pop': 'pop', \n",
+    "    'folk pop': 'pop',\n",
+    "    'norwegian pop': 'pop',\n",
+    "    'acoustic pop': 'pop',\n",
+    "    'dream pop': 'pop',\n",
+    "    'chamber pop': 'pop',\n",
+    "    'german pop': 'pop',\n",
+    "    'bedroom pop': 'pop',\n",
+    "    'city pop': 'pop',\n",
+    "    'dance pop': 'pop',\n",
+    "    'art pop': 'pop',\n",
+    "    'indie pop': 'pop',\n",
+    "\n",
+    "    'alt country': 'country',\n",
+    "    'traditional country': 'country',\n",
+    "\n",
+    "    'blues rock': 'blues',\n",
+    "\n",
+    "    'brooklyn drill': 'drill',\n",
+    "\n",
+    "    'gangster rap': 'rap',\n",
+    "    'memphis rap': 'rap',\n",
+    "    'rock rap': 'rap',\n",
+    "    'meme rap': 'rap',\n",
+    "    'punk rap': 'rap',\n",
+    "    'emo rap': 'rap',\n",
+    "    'jazz rap': 'rap',\n",
+    "    'melodic rap': 'rap',\n",
+    "    'cloud rap': 'rap',\n",
+    "    'k-rap': 'rap',\n",
+    "    'rage rap': 'rap',\n",
+    "\n",
+    "    'edm trap': 'trap',\n",
+    "    'italian trap': 'trap',\n",
+    "    'dark trap': 'trap',\n",
+    "\n",
+    "    'hip-hop': 'hip hop',\n",
+    "    'southern hip hop': 'hip hop',\n",
+    "    'west coast hip hop': 'hip hop',\n",
+    "    'east coast hip hop': 'hip hop',\n",
+    "    'finnish hip hop': 'hip hop',\n",
+    "    'german hip hop': 'hip hop',\n",
+    "    'underground hip hop': 'hip hop',\n",
+    "    'norwegian hip hop': 'hip hop',\n",
+    "    'experimental hip hop': 'hip hop',\n",
+    "    'alternative hip hop': 'hip hop',\n",
+    "    'latin hip hop': 'hip hop',\n",
+    "\n",
+    "    'jazz blues': 'jazz',\n",
+    "    'vocal jazz': 'jazz',\n",
+    "    'french jazz': 'jazz',\n",
+    "    'free jazz': 'jazz',\n",
+    "    'soul jazz': 'jazz',\n",
+    "    'jazz fusion': 'jazz',\n",
+    "    'nu jazz': 'jazz',\n",
+    "    'jazz funk': 'jazz',\n",
+    "    'cool jazz': 'jazz',\n",
+    "    'jazz beats': 'jazz',\n",
+    "    'jazz house': 'jazz',\n",
+    "    'indie jazz': 'jazz',\n",
+    "    'smooth jazz': 'jazz',\n",
+    "\n",
+    "    'melodic techno': 'techno',\n",
+    "    'minimal techno': 'techno',\n",
+    "    'hypertechno': 'techno',\n",
+    "    'hard techno': 'techno',\n",
+    "    'hardcore techno': 'techno',\n",
+    "    'dub techno': 'techno',\n",
+    "\n",
+    "    'future house': 'house',\n",
+    "    'tech house': 'house',\n",
+    "    'bass house': 'house',\n",
+    "    'electro house': 'house',\n",
+    "    'tropical house': 'house',\n",
+    "    'french house': 'house',\n",
+    "    'melodic house': 'house',\n",
+    "    'organic house': 'house',\n",
+    "    'progressive house': 'house',\n",
+    "    'latin house': 'house',\n",
+    "    'deep house': 'house',\n",
+    "    'slap house': 'house',\n",
+    "    'chicago house': 'house'\n",
+    "    \n",
+    "} \n",
+    "\n",
+    "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Map hip-hop to rap -> Models without this are too confused due to the genres being so alike"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "genre_mapping = {\n",
+    "    'hip hop': 'rap'\n",
+    "} \n",
+    "\n",
+    "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels_normalized]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "genre_mapping = {\n",
+    "    'hip-hop': 'hip hop'\n",
+    "} \n",
+    "\n",
+    "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels_normalized]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Or remove hip hop\n",
+    "\n",
+    "X = X[np.array(y_labels_normalized) != \"hip hop\"]\n",
+    "y_labels_normalized = np.array(y_labels_normalized)[np.array(y_labels_normalized) != \"hip hop\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extra: Keep only a popular type of genre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered dataset size: 34761 entries (from 100384 original)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "original_labels_list = y_labels_normalized\n",
+    "y_labels_normalized = np.array(y_labels_normalized)\n",
+    "\n",
+    "\n",
+    "y_lower = np.array([genre.lower() for genre in y_labels_normalized])\n",
+    "\n",
+    "mask = np.array([genre in popular_genres for genre in y_lower])\n",
+    "\n",
+    "X = X[mask]\n",
+    "y_labels_normalized = y_labels_normalized[mask]\n",
+    "\n",
+    "# Optional: print some info\n",
+    "print(f\"Filtered dataset size: {len(y_labels_normalized)} entries (from {len(original_labels_list)} original)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Chart the leftover genres"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import Counter\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "y = y_labels_normalized\n",
+    "\n",
+    "freq_original = Counter(y)\n",
+    "\n",
+    "orig_genres = list(freq_original.keys())\n",
+    "orig_counts = list(freq_original.values())\n",
+    "\n",
+    "fig, axs = plt.subplots(figsize=(15, 6))\n",
+    "\n",
+    "sns.barplot(x=orig_genres, y=orig_counts, ax=axs)\n",
+    "axs.set_title(\"Original Genre Frequencies\")\n",
+    "axs.set_xlabel(\"Genre\")\n",
+    "axs.set_ylabel(\"Count\")\n",
+    "axs.tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Remove all songs with genres which do not come up more than n times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class counts: Counter({'pop': 5097, 'house': 3185, 'rock': 3134, 'metal': 2951, 'techno': 2423, 'rap': 2167, 'hip hop': 1276, 'punk': 1169, 'country': 1167, 'indie': 1152, 'folk': 1151, 'idm': 989, 'alt-rock': 951, 'blues': 948, 'jazz': 944, 'lo-fi': 941, 'funk': 882, 'classical': 851, 'death-metal': 851, 'rock-n-roll': 813, 'phonk': 608, 'soul': 486, 'reggae': 295, 'trap': 259, 'r&b': 40, 'drill': 29, 'acid': 2})\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1490/1997896913.py:51: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  axs.set_xticklabels(le.classes_)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "import numpy as np\n",
+    "from collections import Counter\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# --- Encoding/Standardizing X ---\n",
+    "# Remove compex object variables\n",
+    "if np.iscomplexobj(X):\n",
+    "    X = np.abs(X)\n",
+    "\n",
+    "# Standardize the features\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "\n",
+    "# --- Counting Labels ---\n",
+    "# Check class counts.\n",
+    "counter = Counter(y)\n",
+    "print(\"Class counts:\", counter)\n",
+    "\n",
+    "# Keep only classes with at least n samples. At least 2  or any other % 2 == 0 number\n",
+    "#n = 750 # for mapping with regex\n",
+    "#n = 2000 # for idk\n",
+    "n = 500\n",
+    "classes_to_keep = {cls for cls, count in counter.items() if count >= n}\n",
+    "indices_to_keep = [i for i, label in enumerate(y) if label in classes_to_keep]\n",
+    "\n",
+    "y = np.array(y)\n",
+    "\n",
+    "# Filter the data.\n",
+    "X_filtered = X_scaled[indices_to_keep]\n",
+    "y_filtered = y[indices_to_keep]\n",
+    "\n",
+    "# Encode the Genres into numerical values\n",
+    "le = LabelEncoder()\n",
+    "y_encoded = le.fit_transform(y_filtered)\n",
+    "\n",
+    "freq_leftover = Counter(y_encoded)\n",
+    "leftover_genres = list(freq_leftover.keys())\n",
+    "leftover_counts = list(freq_leftover.values())\n",
+    "\n",
+    "fig, axs = plt.subplots(figsize=(15, 6))\n",
+    "\n",
+    "# Plot normalized label frequencies.\n",
+    "sns.barplot(x=leftover_genres, y=leftover_counts, ax=axs)\n",
+    "axs.set_title(\"Normalized Genre Frequencies\")\n",
+    "axs.set_xlabel(\"Genre\")\n",
+    "axs.set_ylabel(\"Count\")\n",
+    "axs.tick_params(axis='x', rotation=45)\n",
+    "axs.set_xticklabels(le.classes_)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Remove highly collerating data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dropping columns: ['feature_5', 'feature_8', 'feature_10', 'feature_14', 'feature_20', 'feature_23', 'feature_29', 'feature_30', 'feature_32', 'feature_34', 'feature_41', 'feature_43', 'feature_44', 'feature_48', 'feature_50', 'feature_53']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "CORRELATING_VALUE = 0.95\n",
+    "\n",
+    "X = X_filtered\n",
+    "\n",
+    "def remove_highly_correlated_features(X, threshold):\n",
+    "    # If X is a NumPy array, convert it to a DataFrame.\n",
+    "    if isinstance(X, np.ndarray):\n",
+    "        # Create column names if not provided\n",
+    "        X_df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])\n",
+    "    else:\n",
+    "        X_df = X.copy()\n",
+    "    \n",
+    "    # Compute the absolute correlation matrix\n",
+    "    corr_matrix = X_df.corr().abs()\n",
+    "    \n",
+    "    # Create an upper triangle matrix of correlations\n",
+    "    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
+    "    \n",
+    "    # Identify columns to drop: any feature with correlation greater than the threshold\n",
+    "    to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n",
+    "    print(\"Dropping columns:\", to_drop)\n",
+    "    \n",
+    "    # Drop these columns from the dataframe\n",
+    "    X_df_reduced = X_df.drop(columns=to_drop)\n",
+    "    \n",
+    "    # If original X was a NumPy array, return as a NumPy array.\n",
+    "    if isinstance(X, np.ndarray):\n",
+    "        return X_df_reduced.values\n",
+    "    else:\n",
+    "        return X_df_reduced\n",
+    "\n",
+    "\n",
+    "X_reduced = remove_highly_correlated_features(X, threshold=CORRELATING_VALUE)\n",
+    "\n",
+    "# -- Create a feature correlation matrix --\n",
+    "df_features = pd.DataFrame(X_reduced, columns=[f'feature_{i}' for i in range(X_reduced.shape[1])])\n",
+    "\n",
+    "# Compute the correlation matrix.\n",
+    "corr = df_features.corr()\n",
+    "\n",
+    "plt.figure(figsize=(12,10))\n",
+    "sns.heatmap(corr, cmap='coolwarm', annot=False)\n",
+    "plt.title('Feature Correlation Matrix')\n",
+    "plt.show()\n",
+    "\n",
+    "X_filtered = X_reduced\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create a Test-Train split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set shape: (26920, 39) (26920,)\n",
+      "Test set shape: (6730, 39) (6730,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X_filtered, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded\n",
+    ")\n",
+    "\n",
+    "print(\"Training set shape:\", X_train.shape, y_train.shape)\n",
+    "print(\"Test set shape:\", X_test.shape, y_test.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hyperparameter tuning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sequential ANN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 3 Complete [00h 00m 49s]\n",
+      "val_accuracy: 0.4161950349807739\n",
+      "\n",
+      "Best val_accuracy So Far: 0.4161950349807739\n",
+      "Total elapsed time: 00h 02m 11s\n",
+      "\n",
+      "Search: Running Trial #4\n",
+      "\n",
+      "Value             |Best Value So Far |Hyperparameter\n",
+      "tanh              |relu              |activation_1\n",
+      "112               |64                |units_1\n",
+      "0.1               |0.3               |dropout_rate\n",
+      "False             |True              |second_layer\n",
+      "0.001             |0.001             |learning_rate\n",
+      "sigmoid           |sigmoid           |activation_2\n",
+      "32                |80                |units_2\n",
+      "0.2               |0.4               |dropout_rate_2\n",
+      "\n",
+      "Epoch 1/60\n",
+      "\u001b[1m862/862\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.3075 - loss: 2.3225 - val_accuracy: 0.4040 - val_loss: 1.9053\n",
+      "Epoch 2/60\n",
+      "\u001b[1m862/862\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.3973 - loss: 1.9112 - val_accuracy: 0.4097 - val_loss: 1.8687\n",
+      "Epoch 3/60\n",
+      "\u001b[1m862/862\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.4048 - loss: 1.8740 - val_accuracy: 0.4120 - val_loss: 1.8523\n",
+      "Epoch 4/60\n",
+      "\u001b[1m862/862\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.4059 - loss: 1.8595 - val_accuracy: 0.4133 - val_loss: 1.8431\n",
+      "Epoch 5/60\n",
+      "\u001b[1m862/862\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.4100 - loss: 1.8512 - val_accuracy: 0.4175 - val_loss: 1.8356\n",
+      "Epoch 6/60\n",
+      "\u001b[1m862/862\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.4109 - loss: 1.8433 - val_accuracy: 0.4187 - val_loss: 1.8305\n",
+      "Epoch 7/60\n",
+      "\u001b[1m862/862\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.4143 - loss: 1.8393 - val_accuracy: 0.4229 - val_loss: 1.8253\n",
+      "Epoch 8/60\n",
+      "\u001b[1m862/862\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.4139 - loss: 1.8324 - val_accuracy: 0.4229 - val_loss: 1.8194\n",
+      "Epoch 9/60\n",
+      "\u001b[1m862/862\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.4127 - loss: 1.8305 - val_accuracy: 0.4248 - val_loss: 1.8140\n",
+      "Epoch 10/60\n",
+      "\u001b[1m 43/862\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4102 - loss: 1.8058  "
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[13], line 70\u001b[0m\n\u001b[1;32m     67\u001b[0m early_stop \u001b[38;5;241m=\u001b[39m EarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m, restore_best_weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m     69\u001b[0m \u001b[38;5;66;03m# Start hyperparameter search\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m \u001b[43mtuner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mEPOCHS\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Increase epochs for a more robust search if time allows\u001b[39;49;00m\n\u001b[1;32m     74\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mearly_stop\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     75\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;66;03m# Retrieve best hyperparameters and build the best model\u001b[39;00m\n\u001b[1;32m     78\u001b[0m best_hps \u001b[38;5;241m=\u001b[39m tuner\u001b[38;5;241m.\u001b[39mget_best_hyperparameters(num_trials\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/keras_tuner/src/engine/base_tuner.py:234\u001b[0m, in \u001b[0;36mBaseTuner.search\u001b[0;34m(self, *fit_args, **fit_kwargs)\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_trial_begin(trial)\n\u001b[0;32m--> 234\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_try_run_and_update_trial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_trial_end(trial)\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_search_end()\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/keras_tuner/src/engine/base_tuner.py:274\u001b[0m, in \u001b[0;36mBaseTuner._try_run_and_update_trial\u001b[0;34m(self, trial, *fit_args, **fit_kwargs)\u001b[0m\n\u001b[1;32m    272\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_try_run_and_update_trial\u001b[39m(\u001b[38;5;28mself\u001b[39m, trial, \u001b[38;5;241m*\u001b[39mfit_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_kwargs):\n\u001b[1;32m    273\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 274\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_and_update_trial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    275\u001b[0m         trial\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m trial_module\u001b[38;5;241m.\u001b[39mTrialStatus\u001b[38;5;241m.\u001b[39mCOMPLETED\n\u001b[1;32m    276\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/keras_tuner/src/engine/base_tuner.py:239\u001b[0m, in \u001b[0;36mBaseTuner._run_and_update_trial\u001b[0;34m(self, trial, *fit_args, **fit_kwargs)\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_run_and_update_trial\u001b[39m(\u001b[38;5;28mself\u001b[39m, trial, \u001b[38;5;241m*\u001b[39mfit_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_kwargs):\n\u001b[0;32m--> 239\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_trial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    240\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moracle\u001b[38;5;241m.\u001b[39mget_trial(trial\u001b[38;5;241m.\u001b[39mtrial_id)\u001b[38;5;241m.\u001b[39mmetrics\u001b[38;5;241m.\u001b[39mexists(\n\u001b[1;32m    241\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moracle\u001b[38;5;241m.\u001b[39mobjective\u001b[38;5;241m.\u001b[39mname\n\u001b[1;32m    242\u001b[0m     ):\n\u001b[1;32m    243\u001b[0m         \u001b[38;5;66;03m# The oracle is updated by calling `self.oracle.update_trial()` in\u001b[39;00m\n\u001b[1;32m    244\u001b[0m         \u001b[38;5;66;03m# `Tuner.run_trial()`. For backward compatibility, we support this\u001b[39;00m\n\u001b[1;32m    245\u001b[0m         \u001b[38;5;66;03m# use case. No further action needed in this case.\u001b[39;00m\n\u001b[1;32m    246\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    247\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe use case of calling \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    248\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`self.oracle.update_trial(trial_id, metrics)` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    254\u001b[0m             stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m    255\u001b[0m         )\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/keras_tuner/src/engine/tuner.py:314\u001b[0m, in \u001b[0;36mTuner.run_trial\u001b[0;34m(self, trial, *args, **kwargs)\u001b[0m\n\u001b[1;32m    312\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mappend(model_checkpoint)\n\u001b[1;32m    313\u001b[0m     copied_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m callbacks\n\u001b[0;32m--> 314\u001b[0m     obj_value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_build_and_fit_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcopied_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    316\u001b[0m     histories\u001b[38;5;241m.\u001b[39mappend(obj_value)\n\u001b[1;32m    317\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m histories\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/keras_tuner/src/engine/tuner.py:233\u001b[0m, in \u001b[0;36mTuner._build_and_fit_model\u001b[0;34m(self, trial, *args, **kwargs)\u001b[0m\n\u001b[1;32m    231\u001b[0m hp \u001b[38;5;241m=\u001b[39m trial\u001b[38;5;241m.\u001b[39mhyperparameters\n\u001b[1;32m    232\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_try_build(hp)\n\u001b[0;32m--> 233\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhypermodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    235\u001b[0m \u001b[38;5;66;03m# Save the build config for model loading later.\u001b[39;00m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mmulti_backend():\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/keras_tuner/src/engine/hypermodel.py:149\u001b[0m, in \u001b[0;36mHyperModel.fit\u001b[0;34m(self, hp, model, *args, **kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, hp, model, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    126\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Train the model.\u001b[39;00m\n\u001b[1;32m    127\u001b[0m \n\u001b[1;32m    128\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    147\u001b[0m \u001b[38;5;124;03m        If return a float, it should be the `objective` value.\u001b[39;00m\n\u001b[1;32m    148\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 149\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py:371\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator:\n\u001b[1;32m    370\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 371\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    372\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n\u001b[1;32m    373\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/keras/src/backend/tensorflow/trainer.py:222\u001b[0m, in \u001b[0;36mTensorFlowTrainer._make_function.<locals>.function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m opt_outputs\u001b[38;5;241m.\u001b[39mhas_value():\n\u001b[1;32m    221\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n\u001b[0;32m--> 222\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mopt_outputs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    223\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    224\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m step, data \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\n\u001b[1;32m    225\u001b[0m         \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps_per_execution), iterator\n\u001b[1;32m    226\u001b[0m     ):\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/tensorflow/python/data/ops/optional_ops.py:189\u001b[0m, in \u001b[0;36m_OptionalImpl.get_value\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    183\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mname_scope(name, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOptionalGetValue\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    184\u001b[0m                     [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variant_tensor]) \u001b[38;5;28;01mas\u001b[39;00m scope:\n\u001b[1;32m    185\u001b[0m   \u001b[38;5;28;01mwith\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mcolocate_with(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variant_tensor):\n\u001b[1;32m    186\u001b[0m     result \u001b[38;5;241m=\u001b[39m gen_optional_ops\u001b[38;5;241m.\u001b[39moptional_get_value(\n\u001b[1;32m    187\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variant_tensor,\n\u001b[1;32m    188\u001b[0m         name\u001b[38;5;241m=\u001b[39mscope,\n\u001b[0;32m--> 189\u001b[0m         output_types\u001b[38;5;241m=\u001b[39m\u001b[43mstructure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_flat_tensor_types\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_element_spec\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    190\u001b[0m         output_shapes\u001b[38;5;241m=\u001b[39mstructure\u001b[38;5;241m.\u001b[39mget_flat_tensor_shapes(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_element_spec),\n\u001b[1;32m    191\u001b[0m     )\n\u001b[1;32m    192\u001b[0m   \u001b[38;5;66;03m# NOTE: We do not colocate the deserialization of composite tensors\u001b[39;00m\n\u001b[1;32m    193\u001b[0m   \u001b[38;5;66;03m# because not all ops are guaranteed to have non-GPU kernels.\u001b[39;00m\n\u001b[1;32m    194\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m structure\u001b[38;5;241m.\u001b[39mfrom_tensor_list(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_element_spec, result)\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/tensorflow/python/data/util/structure.py:319\u001b[0m, in \u001b[0;36mget_flat_tensor_types\u001b[0;34m(element_spec)\u001b[0m\n\u001b[1;32m    309\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mget_flat_tensor_types\u001b[39m(element_spec):\n\u001b[1;32m    310\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Returns a list `tf.DType`s for the element tensor representation.\u001b[39;00m\n\u001b[1;32m    311\u001b[0m \n\u001b[1;32m    312\u001b[0m \u001b[38;5;124;03m  Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    317\u001b[0m \u001b[38;5;124;03m    A list `tf.DType`s for the element tensor representation.\u001b[39;00m\n\u001b[1;32m    318\u001b[0m \u001b[38;5;124;03m  \"\"\"\u001b[39;00m\n\u001b[0;32m--> 319\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m [spec\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;28;01mfor\u001b[39;00m spec \u001b[38;5;129;01min\u001b[39;00m \u001b[43mget_flat_tensor_specs\u001b[49m\u001b[43m(\u001b[49m\u001b[43melement_spec\u001b[49m\u001b[43m)\u001b[49m]\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/tensorflow/python/data/util/structure.py:293\u001b[0m, in \u001b[0;36mget_flat_tensor_specs\u001b[0;34m(element_spec)\u001b[0m\n\u001b[1;32m    280\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Returns a list `tf.TypeSpec`s for the element tensor representation.\u001b[39;00m\n\u001b[1;32m    281\u001b[0m \n\u001b[1;32m    282\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    287\u001b[0m \u001b[38;5;124;03m  A list `tf.TypeSpec`s for the element tensor representation.\u001b[39;00m\n\u001b[1;32m    288\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    290\u001b[0m \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\n\u001b[1;32m    292\u001b[0m     itertools\u001b[38;5;241m.\u001b[39mchain\u001b[38;5;241m.\u001b[39mfrom_iterable(\n\u001b[0;32m--> 293\u001b[0m         spec\u001b[38;5;241m.\u001b[39m_flat_tensor_specs \u001b[38;5;28;01mfor\u001b[39;00m spec \u001b[38;5;129;01min\u001b[39;00m \u001b[43mnest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflatten\u001b[49m\u001b[43m(\u001b[49m\u001b[43melement_spec\u001b[49m\u001b[43m)\u001b[49m))\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/tensorflow/python/data/util/nest.py:41\u001b[0m, in \u001b[0;36mflatten\u001b[0;34m(structure)\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mis_nested\u001b[39m(structure):\n\u001b[1;32m     38\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m nest_util\u001b[38;5;241m.\u001b[39mis_nested(nest_util\u001b[38;5;241m.\u001b[39mModality\u001b[38;5;241m.\u001b[39mDATA, structure)\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mflatten\u001b[39m(structure):\n\u001b[1;32m     42\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m nest_util\u001b[38;5;241m.\u001b[39mflatten(nest_util\u001b[38;5;241m.\u001b[39mModality\u001b[38;5;241m.\u001b[39mDATA, structure)\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21massert_same_structure\u001b[39m(nest1, nest2, check_types\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import keras_tuner as kt\n",
+    "from tensorflow.keras import layers, models, regularizers\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "from sklearn.metrics import confusion_matrix, classification_report\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "EPOCHS = 60\n",
+    "\n",
+    "# Ensure reproducibility\n",
+    "np.random.seed(42)\n",
+    "tf.random.set_seed(42)\n",
+    "\n",
+    "# Assuming X_train, X_test, y_train, y_test are already defined\n",
+    "num_features = X_train.shape[1]\n",
+    "num_classes = len(np.unique(y_train))\n",
+    "\n",
+    "def build_model(hp):\n",
+    "    model = models.Sequential()\n",
+    "    \n",
+    "    # First layer: Let tuner choose activation function and number of units.\n",
+    "    activation_1 = hp.Choice('activation_1', values=['relu', 'tanh', 'sigmoid'])\n",
+    "    units_1 = hp.Int('units_1', min_value=32, max_value=126, step=16)\n",
+    "    #units_1 = hp.Int('units_1', min_value=32, max_value=256, step=32) # Very Big Model\n",
+    "    model.add(layers.Dense(units_1, activation=activation_1, input_shape=(num_features,),\n",
+    "                           kernel_regularizer=regularizers.l2(0.001)))\n",
+    "    dropout_rate = hp.Float('dropout_rate', 0.0, 0.5, step=0.1)\n",
+    "    model.add(layers.Dropout(dropout_rate))\n",
+    "    \n",
+    "    # Optional second layer: conditionally add if chosen by the tuner\n",
+    "    if hp.Boolean(\"second_layer\"):\n",
+    "        activation_2 = hp.Choice('activation_2', values=['relu', 'tanh', 'sigmoid'])\n",
+    "        units_2 = hp.Int('units_2', min_value=16, max_value=126, step=16)\n",
+    "        #units_2 = hp.Int('units_2', min_value=32, max_value=256, step=32) # Very Big\n",
+    "        model.add(layers.Dense(units_2, activation=activation_2,\n",
+    "                               kernel_regularizer=regularizers.l2(0.001)))\n",
+    "        # Optional dropout after second layer\n",
+    "        dropout_rate_2 = hp.Float('dropout_rate_2', 0.0, 0.5, step=0.1)\n",
+    "        model.add(layers.Dropout(dropout_rate_2))\n",
+    "    \n",
+    "    # Output layer\n",
+    "    model.add(layers.Dense(num_classes, activation='softmax'))\n",
+    "    \n",
+    "    # Hyperparameter for learning rate\n",
+    "    lr = hp.Choice('learning_rate', values = [1e-4, 1e-3, 1e-2])\n",
+    "    \n",
+    "    model.compile(\n",
+    "        optimizer=tf.keras.optimizers.Adam(learning_rate=lr),\n",
+    "        loss='sparse_categorical_crossentropy',\n",
+    "        metrics=['accuracy']\n",
+    "    )\n",
+    "    return model\n",
+    "\n",
+    "# Initialize the tuner\n",
+    "tuner = kt.RandomSearch(\n",
+    "    build_model,\n",
+    "    objective='val_accuracy',\n",
+    "    max_trials=150,\n",
+    "    executions_per_trial=1,\n",
+    "    overwrite=True,\n",
+    "    directory='my_dir',\n",
+    "    project_name='activation_tuning'\n",
+    ")\n",
+    "\n",
+    "# EarlyStopping callback to stop training when validation loss doesn't improve\n",
+    "early_stop = EarlyStopping(monitor='val_loss', patience=4, restore_best_weights=True)\n",
+    "\n",
+    "# Start hyperparameter search\n",
+    "tuner.search(\n",
+    "    X_train, y_train,\n",
+    "    validation_data=(X_test, y_test),\n",
+    "    epochs=EPOCHS,  # Increase epochs for a more robust search if time allows\n",
+    "    callbacks=[early_stop]\n",
+    ")\n",
+    "\n",
+    "# Retrieve best hyperparameters and build the best model\n",
+    "best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]\n",
+    "best_model = tuner.hypermodel.build(best_hps)\n",
+    "print(\"Best Hyperparameters:\")\n",
+    "print(best_hps.values)\n",
+    "\n",
+    "# Train the best model further if needed\n",
+    "history = best_model.fit(\n",
+    "    X_train, y_train,\n",
+    "    validation_data=(X_test, y_test),\n",
+    "    epochs=EPOCHS,\n",
+    "    callbacks=[early_stop],\n",
+    "    #optimzer=tf.keras.optimizers.Adam(learning_rate=0.001)\n",
+    ")\n",
+    "\n",
+    "# Evaluate the best model on the test set\n",
+    "test_loss, test_acc = best_model.evaluate(X_test, y_test)\n",
+    "print(f\"Test accuracy: {test_acc:.4f}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### K-Nearest-Neighbour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters found:  {'metric': 'manhattan', 'n_neighbors': 20, 'weights': 'distance'}\n",
+      "Test accuracy:  0.5115401364494121\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "param_grid = {\n",
+    "    'n_neighbors': list(range(1, 41)),         # Try k from 1 to 30\n",
+    "    'weights': ['uniform', 'distance'],        # Weight function\n",
+    "    'metric': ['euclidean', 'manhattan']       # Distance metrics\n",
+    "}\n",
+    "\n",
+    "knn = KNeighborsClassifier()\n",
+    "grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='accuracy')\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Best parameters found: \", grid_search.best_params_)\n",
+    "\n",
+    "# Predict using best estimator\n",
+    "best_knn = grid_search.best_estimator_\n",
+    "y_pred = best_knn.predict(X_test)\n",
+    "\n",
+    "# Evaluate\n",
+    "print(\"Test accuracy: \", accuracy_score(y_test, y_pred))\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Confusion Matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n",
+    "disp.plot(cmap=plt.cm.Blues)\n",
+    "plt.title(\"KNN Confusion Matrix\")\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Todos:\n",
+    "\n",
+    "Remove Electronic from Features\n",
+    "Avoid Feature Tuning?\n",
+    "Gridsearch on XGradient Boost\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### X-Gradient-Boost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
+      "[CV] END colsample_bytree=0.749816047538945, learning_rate=0.20014286128198325, max_depth=13, n_estimators=288, subsample=0.8387400631785948; total time= 2.9min\n",
+      "[CV] END colsample_bytree=0.749816047538945, learning_rate=0.20014286128198325, max_depth=13, n_estimators=288, subsample=0.8387400631785948; total time= 2.9min\n",
+      "[CV] END colsample_bytree=0.749816047538945, learning_rate=0.20014286128198325, max_depth=13, n_estimators=288, subsample=0.8387400631785948; total time= 2.9min\n",
+      "[CV] END colsample_bytree=0.749816047538945, learning_rate=0.20014286128198325, max_depth=13, n_estimators=288, subsample=0.8387400631785948; total time= 2.9min\n",
+      "[CV] END colsample_bytree=0.749816047538945, learning_rate=0.20014286128198325, max_depth=13, n_estimators=288, subsample=0.8387400631785948; total time= 3.0min\n",
+      "[CV] END colsample_bytree=0.7783331011414365, learning_rate=0.02999498316360058, max_depth=13, n_estimators=199, subsample=0.6571467271687763; total time= 4.7min\n",
+      "[CV] END colsample_bytree=0.7783331011414365, learning_rate=0.02999498316360058, max_depth=13, n_estimators=199, subsample=0.6571467271687763; total time= 4.8min\n",
+      "[CV] END colsample_bytree=0.7783331011414365, learning_rate=0.02999498316360058, max_depth=13, n_estimators=199, subsample=0.6571467271687763; total time= 4.8min\n",
+      "[CV] END colsample_bytree=0.7783331011414365, learning_rate=0.02999498316360058, max_depth=13, n_estimators=199, subsample=0.6571467271687763; total time= 4.8min\n",
+      "[CV] END colsample_bytree=0.7783331011414365, learning_rate=0.02999498316360058, max_depth=13, n_estimators=199, subsample=0.6571467271687763; total time= 4.8min\n",
+      "[CV] END colsample_bytree=0.996884623716487, learning_rate=0.13349630192554332, max_depth=15, n_estimators=121, subsample=0.6028265220878869; total time= 2.5min\n",
+      "[CV] END colsample_bytree=0.996884623716487, learning_rate=0.13349630192554332, max_depth=15, n_estimators=121, subsample=0.6028265220878869; total time= 2.6min\n",
+      "[CV] END colsample_bytree=0.996884623716487, learning_rate=0.13349630192554332, max_depth=15, n_estimators=121, subsample=0.6028265220878869; total time= 2.6min\n",
+      "[CV] END colsample_bytree=0.996884623716487, learning_rate=0.13349630192554332, max_depth=15, n_estimators=121, subsample=0.6028265220878869; total time= 2.5min\n",
+      "[CV] END colsample_bytree=0.996884623716487, learning_rate=0.13349630192554332, max_depth=15, n_estimators=121, subsample=0.6028265220878869; total time= 2.6min\n",
+      "[CV] END colsample_bytree=0.6092249700165663, learning_rate=0.11495493205167782, max_depth=15, n_estimators=287, subsample=0.7465447373174767; total time= 3.7min\n",
+      "[CV] END colsample_bytree=0.6092249700165663, learning_rate=0.11495493205167782, max_depth=15, n_estimators=287, subsample=0.7465447373174767; total time= 3.8min\n",
+      "[CV] END colsample_bytree=0.8603553891795411, learning_rate=0.021282315805420053, max_depth=13, n_estimators=393, subsample=0.6003115063364057; total time= 8.9min\n",
+      "[CV] END colsample_bytree=0.8603553891795411, learning_rate=0.021282315805420053, max_depth=13, n_estimators=393, subsample=0.6003115063364057; total time= 8.9min\n",
+      "[CV] END colsample_bytree=0.7824279936868144, learning_rate=0.16703519227860275, max_depth=8, n_estimators=154, subsample=0.9932923543227152; total time= 1.6min\n",
+      "[CV] END colsample_bytree=0.7824279936868144, learning_rate=0.16703519227860275, max_depth=8, n_estimators=154, subsample=0.9932923543227152; total time= 1.7min\n",
+      "[CV] END colsample_bytree=0.6092249700165663, learning_rate=0.11495493205167782, max_depth=15, n_estimators=287, subsample=0.7465447373174767; total time= 3.8min\n",
+      "[CV] END colsample_bytree=0.6092249700165663, learning_rate=0.11495493205167782, max_depth=15, n_estimators=287, subsample=0.7465447373174767; total time= 3.8min\n",
+      "[CV] END colsample_bytree=0.7824279936868144, learning_rate=0.16703519227860275, max_depth=8, n_estimators=154, subsample=0.9932923543227152; total time= 1.6min\n",
+      "[CV] END colsample_bytree=0.7824279936868144, learning_rate=0.16703519227860275, max_depth=8, n_estimators=154, subsample=0.9932923543227152; total time= 1.6min\n",
+      "[CV] END colsample_bytree=0.7824279936868144, learning_rate=0.16703519227860275, max_depth=8, n_estimators=154, subsample=0.9932923543227152; total time= 1.6min\n",
+      "[CV] END colsample_bytree=0.786705157299192, learning_rate=0.18198808134726413, max_depth=12, n_estimators=120, subsample=0.7801997007878172; total time= 1.9min\n",
+      "[CV] END colsample_bytree=0.786705157299192, learning_rate=0.18198808134726413, max_depth=12, n_estimators=120, subsample=0.7801997007878172; total time= 1.9min\n",
+      "[CV] END colsample_bytree=0.786705157299192, learning_rate=0.18198808134726413, max_depth=12, n_estimators=120, subsample=0.7801997007878172; total time= 1.9min\n",
+      "[CV] END colsample_bytree=0.6092249700165663, learning_rate=0.11495493205167782, max_depth=15, n_estimators=287, subsample=0.7465447373174767; total time= 3.7min\n",
+      "[CV] END colsample_bytree=0.786705157299192, learning_rate=0.18198808134726413, max_depth=12, n_estimators=120, subsample=0.7801997007878172; total time= 1.9min\n",
+      "[CV] END colsample_bytree=0.786705157299192, learning_rate=0.18198808134726413, max_depth=12, n_estimators=120, subsample=0.7801997007878172; total time= 1.9min\n",
+      "[CV] END colsample_bytree=0.8603553891795411, learning_rate=0.021282315805420053, max_depth=13, n_estimators=393, subsample=0.6003115063364057; total time= 8.7min\n",
+      "[CV] END colsample_bytree=0.8603553891795411, learning_rate=0.021282315805420053, max_depth=13, n_estimators=393, subsample=0.6003115063364057; total time= 8.7min\n",
+      "[CV] END colsample_bytree=0.8603553891795411, learning_rate=0.021282315805420053, max_depth=13, n_estimators=393, subsample=0.6003115063364057; total time= 8.9min\n",
+      "[CV] END colsample_bytree=0.6053059844639466, learning_rate=0.19844035113697056, max_depth=7, n_estimators=364, subsample=0.6063865008880857; total time= 2.3min\n",
+      "[CV] END colsample_bytree=0.6053059844639466, learning_rate=0.19844035113697056, max_depth=7, n_estimators=364, subsample=0.6063865008880857; total time= 2.3min\n",
+      "[CV] END colsample_bytree=0.6053059844639466, learning_rate=0.19844035113697056, max_depth=7, n_estimators=364, subsample=0.6063865008880857; total time= 2.3min\n",
+      "[CV] END colsample_bytree=0.6053059844639466, learning_rate=0.19844035113697056, max_depth=7, n_estimators=364, subsample=0.6063865008880857; total time= 2.2min\n",
+      "[CV] END colsample_bytree=0.6053059844639466, learning_rate=0.19844035113697056, max_depth=7, n_estimators=364, subsample=0.6063865008880857; total time= 2.3min\n",
+      "[CV] END colsample_bytree=0.9637281608315128, learning_rate=0.061755996320003385, max_depth=13, n_estimators=101, subsample=0.7700623497964979; total time= 2.8min\n",
+      "[CV] END colsample_bytree=0.9637281608315128, learning_rate=0.061755996320003385, max_depth=13, n_estimators=101, subsample=0.7700623497964979; total time= 2.7min\n",
+      "[CV] END colsample_bytree=0.9637281608315128, learning_rate=0.061755996320003385, max_depth=13, n_estimators=101, subsample=0.7700623497964979; total time= 2.7min\n",
+      "[CV] END colsample_bytree=0.9637281608315128, learning_rate=0.061755996320003385, max_depth=13, n_estimators=101, subsample=0.7700623497964979; total time= 2.7min\n",
+      "[CV] END colsample_bytree=0.6831766651472755, learning_rate=0.1235400655639983, max_depth=7, n_estimators=317, subsample=0.7799016533479063; total time= 2.4min\n",
+      "[CV] END colsample_bytree=0.6831766651472755, learning_rate=0.1235400655639983, max_depth=7, n_estimators=317, subsample=0.7799016533479063; total time= 2.4min\n",
+      "[CV] END colsample_bytree=0.9637281608315128, learning_rate=0.061755996320003385, max_depth=13, n_estimators=101, subsample=0.7700623497964979; total time= 2.8min\n",
+      "[CV] END colsample_bytree=0.6923575302488596, learning_rate=0.05820509320520235, max_depth=12, n_estimators=363, subsample=0.6137554084460873; total time= 5.3min\n",
+      "[CV] END colsample_bytree=0.6923575302488596, learning_rate=0.05820509320520235, max_depth=12, n_estimators=363, subsample=0.6137554084460873; total time= 5.3min\n",
+      "[CV] END colsample_bytree=0.6923575302488596, learning_rate=0.05820509320520235, max_depth=12, n_estimators=363, subsample=0.6137554084460873; total time= 5.4min\n",
+      "[CV] END colsample_bytree=0.6923575302488596, learning_rate=0.05820509320520235, max_depth=12, n_estimators=363, subsample=0.6137554084460873; total time= 5.3min\n",
+      "[CV] END colsample_bytree=0.6831766651472755, learning_rate=0.1235400655639983, max_depth=7, n_estimators=317, subsample=0.7799016533479063; total time= 2.5min\n",
+      "[CV] END colsample_bytree=0.6831766651472755, learning_rate=0.1235400655639983, max_depth=7, n_estimators=317, subsample=0.7799016533479063; total time= 2.4min\n",
+      "[CV] END colsample_bytree=0.6923575302488596, learning_rate=0.05820509320520235, max_depth=12, n_estimators=363, subsample=0.6137554084460873; total time= 5.3min\n",
+      "[CV] END colsample_bytree=0.6831766651472755, learning_rate=0.1235400655639983, max_depth=7, n_estimators=317, subsample=0.7799016533479063; total time= 2.4min\n",
+      "[CV] END colsample_bytree=0.9844688097397396, learning_rate=0.1789067697356303, max_depth=7, n_estimators=152, subsample=0.8347004662655393; total time= 1.4min\n",
+      "[CV] END colsample_bytree=0.9844688097397396, learning_rate=0.1789067697356303, max_depth=7, n_estimators=152, subsample=0.8347004662655393; total time= 1.4min\n",
+      "[CV] END colsample_bytree=0.9844688097397396, learning_rate=0.1789067697356303, max_depth=7, n_estimators=152, subsample=0.8347004662655393; total time= 1.3min\n",
+      "[CV] END colsample_bytree=0.7580600944007257, learning_rate=0.19533177315875885, max_depth=13, n_estimators=314, subsample=0.8083337040103294; total time= 2.9min\n",
+      "[CV] END colsample_bytree=0.9844688097397396, learning_rate=0.1789067697356303, max_depth=7, n_estimators=152, subsample=0.8347004662655393; total time= 1.4min\n",
+      "[CV] END colsample_bytree=0.9844688097397396, learning_rate=0.1789067697356303, max_depth=7, n_estimators=152, subsample=0.8347004662655393; total time= 1.4min\n",
+      "[CV] END colsample_bytree=0.7580600944007257, learning_rate=0.19533177315875885, max_depth=13, n_estimators=314, subsample=0.8083337040103294; total time= 2.9min\n",
+      "[CV] END colsample_bytree=0.7580600944007257, learning_rate=0.19533177315875885, max_depth=13, n_estimators=314, subsample=0.8083337040103294; total time= 2.8min\n",
+      "[CV] END colsample_bytree=0.7580600944007257, learning_rate=0.19533177315875885, max_depth=13, n_estimators=314, subsample=0.8083337040103294; total time= 2.9min\n",
+      "[CV] END colsample_bytree=0.7580600944007257, learning_rate=0.19533177315875885, max_depth=13, n_estimators=314, subsample=0.8083337040103294; total time= 3.0min\n",
+      "[CV] END colsample_bytree=0.602208846849441, learning_rate=0.17309228569096685, max_depth=6, n_estimators=262, subsample=0.9160702162124823; total time= 1.5min\n",
+      "[CV] END colsample_bytree=0.6298202574719083, learning_rate=0.20737738732010347, max_depth=13, n_estimators=228, subsample=0.679486272613669; total time= 2.3min\n",
+      "[CV] END colsample_bytree=0.6298202574719083, learning_rate=0.20737738732010347, max_depth=13, n_estimators=228, subsample=0.679486272613669; total time= 2.3min\n",
+      "[CV] END colsample_bytree=0.9861021229056552, learning_rate=0.13140684953733694, max_depth=14, n_estimators=256, subsample=0.9208787923016158; total time= 3.6min\n",
+      "[CV] END colsample_bytree=0.6298202574719083, learning_rate=0.20737738732010347, max_depth=13, n_estimators=228, subsample=0.679486272613669; total time= 2.3min\n",
+      "[CV] END colsample_bytree=0.6298202574719083, learning_rate=0.20737738732010347, max_depth=13, n_estimators=228, subsample=0.679486272613669; total time= 2.3min\n",
+      "[CV] END colsample_bytree=0.6298202574719083, learning_rate=0.20737738732010347, max_depth=13, n_estimators=228, subsample=0.679486272613669; total time= 2.3min\n",
+      "[CV] END colsample_bytree=0.602208846849441, learning_rate=0.17309228569096685, max_depth=6, n_estimators=262, subsample=0.9160702162124823; total time= 1.5min\n",
+      "[CV] END colsample_bytree=0.9861021229056552, learning_rate=0.13140684953733694, max_depth=14, n_estimators=256, subsample=0.9208787923016158; total time= 3.6min\n",
+      "[CV] END colsample_bytree=0.9861021229056552, learning_rate=0.13140684953733694, max_depth=14, n_estimators=256, subsample=0.9208787923016158; total time= 3.7min\n",
+      "[CV] END colsample_bytree=0.602208846849441, learning_rate=0.17309228569096685, max_depth=6, n_estimators=262, subsample=0.9160702162124823; total time= 1.4min\n",
+      "[CV] END colsample_bytree=0.9861021229056552, learning_rate=0.13140684953733694, max_depth=14, n_estimators=256, subsample=0.9208787923016158; total time= 3.7min\n",
+      "[CV] END colsample_bytree=0.9861021229056552, learning_rate=0.13140684953733694, max_depth=14, n_estimators=256, subsample=0.9208787923016158; total time= 3.7min\n",
+      "[CV] END colsample_bytree=0.602208846849441, learning_rate=0.17309228569096685, max_depth=6, n_estimators=262, subsample=0.9160702162124823; total time= 1.4min\n",
+      "[CV] END colsample_bytree=0.602208846849441, learning_rate=0.17309228569096685, max_depth=6, n_estimators=262, subsample=0.9160702162124823; total time= 1.4min\n",
+      "[CV] END colsample_bytree=0.9400154311159197, learning_rate=0.09989013482764068, max_depth=6, n_estimators=147, subsample=0.7483273008793065; total time= 1.0min\n",
+      "[CV] END colsample_bytree=0.9400154311159197, learning_rate=0.09989013482764068, max_depth=6, n_estimators=147, subsample=0.7483273008793065; total time= 1.1min\n",
+      "[CV] END colsample_bytree=0.9400154311159197, learning_rate=0.09989013482764068, max_depth=6, n_estimators=147, subsample=0.7483273008793065; total time= 1.0min\n",
+      "[CV] END colsample_bytree=0.9400154311159197, learning_rate=0.09989013482764068, max_depth=6, n_estimators=147, subsample=0.7483273008793065; total time= 1.1min\n",
+      "[CV] END colsample_bytree=0.8423839899124046, learning_rate=0.19526017570266982, max_depth=15, n_estimators=140, subsample=0.9659838702175123; total time= 2.1min\n",
+      "[CV] END colsample_bytree=0.8423839899124046, learning_rate=0.19526017570266982, max_depth=15, n_estimators=140, subsample=0.9659838702175123; total time= 2.1min\n",
+      "[CV] END colsample_bytree=0.9400154311159197, learning_rate=0.09989013482764068, max_depth=6, n_estimators=147, subsample=0.7483273008793065; total time= 1.0min\n",
+      "[CV] END colsample_bytree=0.8423839899124046, learning_rate=0.19526017570266982, max_depth=15, n_estimators=140, subsample=0.9659838702175123; total time= 2.3min\n",
+      "[CV] END colsample_bytree=0.8423839899124046, learning_rate=0.19526017570266982, max_depth=15, n_estimators=140, subsample=0.9659838702175123; total time= 2.2min\n",
+      "[CV] END colsample_bytree=0.8423839899124046, learning_rate=0.19526017570266982, max_depth=15, n_estimators=140, subsample=0.9659838702175123; total time= 2.2min\n",
+      "[CV] END colsample_bytree=0.8675365010654429, learning_rate=0.14318447132349935, max_depth=13, n_estimators=313, subsample=0.9548850970305306; total time= 3.7min\n",
+      "[CV] END colsample_bytree=0.8675365010654429, learning_rate=0.14318447132349935, max_depth=13, n_estimators=313, subsample=0.9548850970305306; total time= 3.6min\n",
+      "[CV] END colsample_bytree=0.8675365010654429, learning_rate=0.14318447132349935, max_depth=13, n_estimators=313, subsample=0.9548850970305306; total time= 3.7min\n",
+      "[CV] END colsample_bytree=0.8675365010654429, learning_rate=0.14318447132349935, max_depth=13, n_estimators=313, subsample=0.9548850970305306; total time= 3.7min\n",
+      "[CV] END colsample_bytree=0.8675365010654429, learning_rate=0.14318447132349935, max_depth=13, n_estimators=313, subsample=0.9548850970305306; total time= 3.7min\n",
+      "[CV] END colsample_bytree=0.7888859700647797, learning_rate=0.03391884918766034, max_depth=8, n_estimators=356, subsample=0.8886918084659492; total time= 4.2min\n",
+      "[CV] END colsample_bytree=0.7888859700647797, learning_rate=0.03391884918766034, max_depth=8, n_estimators=356, subsample=0.8886918084659492; total time= 4.2min\n",
+      "[CV] END colsample_bytree=0.7888859700647797, learning_rate=0.03391884918766034, max_depth=8, n_estimators=356, subsample=0.8886918084659492; total time= 4.2min\n",
+      "[CV] END colsample_bytree=0.7888859700647797, learning_rate=0.03391884918766034, max_depth=8, n_estimators=356, subsample=0.8886918084659492; total time= 4.2min\n",
+      "[CV] END colsample_bytree=0.7888859700647797, learning_rate=0.03391884918766034, max_depth=8, n_estimators=356, subsample=0.8886918084659492; total time= 4.3min\n",
+      "[CV] END colsample_bytree=0.6943939678995823, learning_rate=0.0612136645522648, max_depth=14, n_estimators=306, subsample=0.7710164073434198; total time= 5.6min\n",
+      "[CV] END colsample_bytree=0.6943939678995823, learning_rate=0.0612136645522648, max_depth=14, n_estimators=306, subsample=0.7710164073434198; total time= 5.8min\n",
+      "[CV] END colsample_bytree=0.8034282764658811, learning_rate=0.1915132947852186, max_depth=12, n_estimators=330, subsample=0.7641531692142519; total time= 3.3min\n",
+      "[CV] END colsample_bytree=0.8034282764658811, learning_rate=0.1915132947852186, max_depth=12, n_estimators=330, subsample=0.7641531692142519; total time= 3.4min\n",
+      "[CV] END colsample_bytree=0.6943939678995823, learning_rate=0.0612136645522648, max_depth=14, n_estimators=306, subsample=0.7710164073434198; total time= 5.8min\n",
+      "[CV] END colsample_bytree=0.6943939678995823, learning_rate=0.0612136645522648, max_depth=14, n_estimators=306, subsample=0.7710164073434198; total time= 5.7min\n",
+      "[CV] END colsample_bytree=0.8034282764658811, learning_rate=0.1915132947852186, max_depth=12, n_estimators=330, subsample=0.7641531692142519; total time= 3.4min\n",
+      "[CV] END colsample_bytree=0.6943939678995823, learning_rate=0.0612136645522648, max_depth=14, n_estimators=306, subsample=0.7710164073434198; total time= 6.0min\n",
+      "[CV] END colsample_bytree=0.8034282764658811, learning_rate=0.1915132947852186, max_depth=12, n_estimators=330, subsample=0.7641531692142519; total time= 3.3min\n",
+      "[CV] END colsample_bytree=0.610167650697638, learning_rate=0.031578285398660894, max_depth=12, n_estimators=340, subsample=0.7257423924305306; total time= 6.7min\n",
+      "[CV] END colsample_bytree=0.610167650697638, learning_rate=0.031578285398660894, max_depth=12, n_estimators=340, subsample=0.7257423924305306; total time= 6.7min\n",
+      "[CV] END colsample_bytree=0.8034282764658811, learning_rate=0.1915132947852186, max_depth=12, n_estimators=330, subsample=0.7641531692142519; total time= 3.2min\n",
+      "[CV] END colsample_bytree=0.610167650697638, learning_rate=0.031578285398660894, max_depth=12, n_estimators=340, subsample=0.7257423924305306; total time= 6.7min\n",
+      "[CV] END colsample_bytree=0.610167650697638, learning_rate=0.031578285398660894, max_depth=12, n_estimators=340, subsample=0.7257423924305306; total time= 6.7min\n",
+      "[CV] END colsample_bytree=0.610167650697638, learning_rate=0.031578285398660894, max_depth=12, n_estimators=340, subsample=0.7257423924305306; total time= 6.7min\n",
+      "[CV] END colsample_bytree=0.9521871356061031, learning_rate=0.13487080962675865, max_depth=14, n_estimators=233, subsample=0.782613828193164; total time= 3.6min\n",
+      "[CV] END colsample_bytree=0.9521871356061031, learning_rate=0.13487080962675865, max_depth=14, n_estimators=233, subsample=0.782613828193164; total time= 3.6min\n",
+      "[CV] END colsample_bytree=0.9022204554172195, learning_rate=0.0557596330983245, max_depth=12, n_estimators=286, subsample=0.8779139732158818; total time= 5.8min\n",
+      "[CV] END colsample_bytree=0.9521871356061031, learning_rate=0.13487080962675865, max_depth=14, n_estimators=233, subsample=0.782613828193164; total time= 3.6min\n",
+      "[CV] END colsample_bytree=0.9521871356061031, learning_rate=0.13487080962675865, max_depth=14, n_estimators=233, subsample=0.782613828193164; total time= 3.5min\n",
+      "[CV] END colsample_bytree=0.9521871356061031, learning_rate=0.13487080962675865, max_depth=14, n_estimators=233, subsample=0.782613828193164; total time= 3.5min\n",
+      "[CV] END colsample_bytree=0.9022204554172195, learning_rate=0.0557596330983245, max_depth=12, n_estimators=286, subsample=0.8779139732158818; total time= 5.8min\n",
+      "[CV] END colsample_bytree=0.9022204554172195, learning_rate=0.0557596330983245, max_depth=12, n_estimators=286, subsample=0.8779139732158818; total time= 5.9min\n",
+      "[CV] END colsample_bytree=0.9022204554172195, learning_rate=0.0557596330983245, max_depth=12, n_estimators=286, subsample=0.8779139732158818; total time= 5.8min\n",
+      "[CV] END colsample_bytree=0.9022204554172195, learning_rate=0.0557596330983245, max_depth=12, n_estimators=286, subsample=0.8779139732158818; total time= 5.8min\n",
+      "[CV] END colsample_bytree=0.6873761748867334, learning_rate=0.09330198957407325, max_depth=15, n_estimators=330, subsample=0.9627313766183017; total time= 4.9min\n",
+      "[CV] END colsample_bytree=0.6873761748867334, learning_rate=0.09330198957407325, max_depth=15, n_estimators=330, subsample=0.9627313766183017; total time= 4.9min\n",
+      "[CV] END colsample_bytree=0.7088528997538541, learning_rate=0.13953802410827248, max_depth=14, n_estimators=215, subsample=0.7410275425336676; total time= 3.3min\n",
+      "[CV] END colsample_bytree=0.7088528997538541, learning_rate=0.13953802410827248, max_depth=14, n_estimators=215, subsample=0.7410275425336676; total time= 3.2min\n",
+      "[CV] END colsample_bytree=0.7088528997538541, learning_rate=0.13953802410827248, max_depth=14, n_estimators=215, subsample=0.7410275425336676; total time= 3.3min\n",
+      "[CV] END colsample_bytree=0.7088528997538541, learning_rate=0.13953802410827248, max_depth=14, n_estimators=215, subsample=0.7410275425336676; total time= 3.3min\n",
+      "[CV] END colsample_bytree=0.7088528997538541, learning_rate=0.13953802410827248, max_depth=14, n_estimators=215, subsample=0.7410275425336676; total time= 3.3min\n",
+      "[CV] END colsample_bytree=0.6873761748867334, learning_rate=0.09330198957407325, max_depth=15, n_estimators=330, subsample=0.9627313766183017; total time= 5.2min\n",
+      "[CV] END colsample_bytree=0.6873761748867334, learning_rate=0.09330198957407325, max_depth=15, n_estimators=330, subsample=0.9627313766183017; total time= 5.0min\n",
+      "[CV] END colsample_bytree=0.6873761748867334, learning_rate=0.09330198957407325, max_depth=15, n_estimators=330, subsample=0.9627313766183017; total time= 5.1min\n",
+      "[CV] END colsample_bytree=0.881207583558071, learning_rate=0.08272592047585879, max_depth=6, n_estimators=151, subsample=0.6987504251354405; total time= 1.2min\n",
+      "[CV] END colsample_bytree=0.881207583558071, learning_rate=0.08272592047585879, max_depth=6, n_estimators=151, subsample=0.6987504251354405; total time= 1.2min\n",
+      "[CV] END colsample_bytree=0.881207583558071, learning_rate=0.08272592047585879, max_depth=6, n_estimators=151, subsample=0.6987504251354405; total time= 1.1min\n",
+      "[CV] END colsample_bytree=0.881207583558071, learning_rate=0.08272592047585879, max_depth=6, n_estimators=151, subsample=0.6987504251354405; total time= 1.1min\n",
+      "[CV] END colsample_bytree=0.7219125032632117, learning_rate=0.04293117062858835, max_depth=14, n_estimators=264, subsample=0.8769744131561081; total time= 6.4min\n",
+      "[CV] END colsample_bytree=0.7219125032632117, learning_rate=0.04293117062858835, max_depth=14, n_estimators=264, subsample=0.8769744131561081; total time= 6.5min\n",
+      "[CV] END colsample_bytree=0.7077649335194086, learning_rate=0.05882510444955484, max_depth=11, n_estimators=259, subsample=0.8075162486973464; total time= 4.5min\n",
+      "[CV] END colsample_bytree=0.7077649335194086, learning_rate=0.05882510444955484, max_depth=11, n_estimators=259, subsample=0.8075162486973464; total time= 4.5min\n",
+      "[CV] END colsample_bytree=0.881207583558071, learning_rate=0.08272592047585879, max_depth=6, n_estimators=151, subsample=0.6987504251354405; total time= 1.1min\n",
+      "[CV] END colsample_bytree=0.7219125032632117, learning_rate=0.04293117062858835, max_depth=14, n_estimators=264, subsample=0.8769744131561081; total time= 6.3min\n",
+      "[CV] END colsample_bytree=0.7219125032632117, learning_rate=0.04293117062858835, max_depth=14, n_estimators=264, subsample=0.8769744131561081; total time= 6.4min\n",
+      "[CV] END colsample_bytree=0.7077649335194086, learning_rate=0.05882510444955484, max_depth=11, n_estimators=259, subsample=0.8075162486973464; total time= 4.4min\n",
+      "[CV] END colsample_bytree=0.7077649335194086, learning_rate=0.05882510444955484, max_depth=11, n_estimators=259, subsample=0.8075162486973464; total time= 4.5min\n",
+      "[CV] END colsample_bytree=0.7077649335194086, learning_rate=0.05882510444955484, max_depth=11, n_estimators=259, subsample=0.8075162486973464; total time= 4.4min\n",
+      "[CV] END colsample_bytree=0.7219125032632117, learning_rate=0.04293117062858835, max_depth=14, n_estimators=264, subsample=0.8769744131561081; total time= 6.3min\n",
+      "[CV] END colsample_bytree=0.8785217091359153, learning_rate=0.15245411798488842, max_depth=10, n_estimators=212, subsample=0.9990961940195767; total time= 2.9min\n",
+      "[CV] END colsample_bytree=0.8785217091359153, learning_rate=0.15245411798488842, max_depth=10, n_estimators=212, subsample=0.9990961940195767; total time= 2.7min\n",
+      "[CV] END colsample_bytree=0.8785217091359153, learning_rate=0.15245411798488842, max_depth=10, n_estimators=212, subsample=0.9990961940195767; total time= 2.7min\n",
+      "[CV] END colsample_bytree=0.8785217091359153, learning_rate=0.15245411798488842, max_depth=10, n_estimators=212, subsample=0.9990961940195767; total time= 2.7min\n",
+      "[CV] END colsample_bytree=0.8785217091359153, learning_rate=0.15245411798488842, max_depth=10, n_estimators=212, subsample=0.9990961940195767; total time= 2.7min\n",
+      "[CV] END colsample_bytree=0.706712405710114, learning_rate=0.2053229911665306, max_depth=11, n_estimators=323, subsample=0.9633063543866615; total time= 2.8min\n",
+      "[CV] END colsample_bytree=0.706712405710114, learning_rate=0.2053229911665306, max_depth=11, n_estimators=323, subsample=0.9633063543866615; total time= 2.8min\n",
+      "[CV] END colsample_bytree=0.706712405710114, learning_rate=0.2053229911665306, max_depth=11, n_estimators=323, subsample=0.9633063543866615; total time= 2.9min\n",
+      "[CV] END colsample_bytree=0.706712405710114, learning_rate=0.2053229911665306, max_depth=11, n_estimators=323, subsample=0.9633063543866615; total time= 2.9min\n",
+      "[CV] END colsample_bytree=0.706712405710114, learning_rate=0.2053229911665306, max_depth=11, n_estimators=323, subsample=0.9633063543866615; total time= 2.9min\n",
+      "[CV] END colsample_bytree=0.9212559025519583, learning_rate=0.10406012688920768, max_depth=7, n_estimators=297, subsample=0.7595297769778212; total time= 2.6min\n",
+      "[CV] END colsample_bytree=0.9212559025519583, learning_rate=0.10406012688920768, max_depth=7, n_estimators=297, subsample=0.7595297769778212; total time= 2.7min\n"
+     ]
+    }
+   ],
+   "source": [
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from scipy.stats import uniform, randint\n",
+    "\n",
+    "#param_grid = {\n",
+    "#    'n_estimators': [100, 200, 300, 400],\n",
+    "#    'max_depth': [9, 11, 13, 15],\n",
+    "#    'learning_rate': [0.01, 0.1, 0.2],\n",
+    "#    'subsample': [0.6, 0.8, 1.0],\n",
+    "#    'colsample_bytree': [0.6, 0.8, 1.0]\n",
+    "#}\n",
+    "\n",
+    "param_dist = {\n",
+    "    'n_estimators': randint(100, 400),\n",
+    "    'max_depth': randint(6, 16),\n",
+    "    'learning_rate': uniform(0.01, 0.2),\n",
+    "    'subsample': uniform(0.6, 0.4),  # from 0.6 to 1.0\n",
+    "    'colsample_bytree': uniform(0.6, 0.4)\n",
+    "}\n",
+    "\n",
+    "xgb = XGBClassifier(eval_metric='logloss')\n",
+    "#grid_search = GridSearchCV(xgb, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=2)\n",
+    "#grid_search.fit(X_train, y_train)\n",
+    "random_search = RandomizedSearchCV(\n",
+    "    xgb, param_distributions=param_dist, n_iter=50, cv=5,\n",
+    "    scoring='accuracy', n_jobs=-1, verbose=2, random_state=42\n",
+    ")\n",
+    "random_search.fit(X_train, y_train)\n",
+    "\n",
+    "\n",
+    "print(\"Best parameters found: \", random_search.best_params_)\n",
+    "\n",
+    "# Predict with the best estimator\n",
+    "best_xgb = random_search.best_estimator_\n",
+    "y_pred = best_xgb.predict(X_test)\n",
+    "\n",
+    "# Accuracy\n",
+    "print(\"Test accuracy: \", accuracy_score(y_test, y_pred))\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Confusion Matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n",
+    "disp.plot(cmap=plt.cm.Oranges)\n",
+    "plt.title(\"XGBoost Confusion Matrix\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.5873983739837398\n",
+      "Precision: 0.6771452841677659\n",
+      "Recall: 0.5272963803029447\n",
+      "F1 Score: 0.5631642608791545\n",
+      "\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "       blues       0.00      0.00      0.00         0\n",
+      "   classical       0.00      0.00      0.00         0\n",
+      "     country       0.00      0.00      0.00         0\n",
+      " death-metal       0.00      0.00      0.00         0\n",
+      "  electronic       0.00      0.00      0.00         0\n",
+      "        folk       0.00      0.00      0.00         0\n",
+      "        funk       0.00      0.00      0.00         0\n",
+      "     hip hop       0.00      0.00      0.00         0\n",
+      "       house       0.00      0.00      0.00         0\n",
+      "         idm       0.00      0.00      0.00         0\n",
+      "       indie       0.00      0.00      0.00         0\n",
+      "        jazz       0.00      0.00      0.00         0\n",
+      "       lo-fi       0.00      0.00      0.00         0\n",
+      "       metal       0.00      0.00      0.00         0\n",
+      "       phonk       0.00      0.00      0.00         0\n",
+      "         pop       0.00      0.00      0.00         0\n",
+      "        punk       0.00      0.00      0.00         0\n",
+      "         rap       0.00      0.00      0.00         0\n",
+      "        rock       0.00      0.00      0.00         0\n",
+      "      techno       0.00      0.00      0.00         0\n",
+      "\n",
+      "   micro avg       0.00      0.00      0.00         0\n",
+      "   macro avg       0.00      0.00      0.00         0\n",
+      "weighted avg       0.00      0.00      0.00         0\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cross-validated AUC scores: [0.5646888  0.56660617 0.54555354 0.56787659 0.56969147]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[14], line 60\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# Learning Curve\u001b[39;00m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m learning_curve\n\u001b[0;32m---> 60\u001b[0m train_sizes, train_scores, test_scores \u001b[38;5;241m=\u001b[39m \u001b[43mlearning_curve\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     61\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel_xgb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscoring\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43maccuracy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[1;32m     62\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:216\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    211\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m    212\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    213\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    214\u001b[0m         )\n\u001b[1;32m    215\u001b[0m     ):\n\u001b[0;32m--> 216\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[1;32m    221\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[1;32m    222\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[1;32m    223\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    224\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    225\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m    226\u001b[0m     )\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:2085\u001b[0m, in \u001b[0;36mlearning_curve\u001b[0;34m(estimator, X, y, groups, train_sizes, cv, scoring, exploit_incremental_learning, n_jobs, pre_dispatch, verbose, shuffle, random_state, error_score, return_times, fit_params, params)\u001b[0m\n\u001b[1;32m   2082\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m n_train_samples \u001b[38;5;129;01min\u001b[39;00m train_sizes_abs:\n\u001b[1;32m   2083\u001b[0m         train_test_proportions\u001b[38;5;241m.\u001b[39mappend((train[:n_train_samples], test))\n\u001b[0;32m-> 2085\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2086\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_and_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2087\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2088\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2089\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2090\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscorer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscorer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2091\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2092\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2093\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2094\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   2095\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfit_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrouted_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2096\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscore_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrouted_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscorer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2097\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_train_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   2098\u001b[0m \u001b[43m        \u001b[49m\u001b[43merror_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merror_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2099\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2100\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2101\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrain_test_proportions\u001b[49m\n\u001b[1;32m   2102\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2103\u001b[0m _warn_or_raise_about_fit_failures(results, error_score)\n\u001b[1;32m   2104\u001b[0m results \u001b[38;5;241m=\u001b[39m _aggregate_score_dicts(results)\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/utils/parallel.py:77\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     72\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[1;32m     73\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m     74\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[1;32m     75\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[1;32m     76\u001b[0m )\n\u001b[0;32m---> 77\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/joblib/parallel.py:1918\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1916\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[1;32m   1917\u001b[0m     \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 1918\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1920\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[1;32m   1921\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[1;32m   1922\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[1;32m   1923\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[1;32m   1924\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[1;32m   1925\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/joblib/parallel.py:1847\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1845\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1846\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m-> 1847\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1848\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_completed_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1849\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_progress()\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/utils/parallel.py:139\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    137\u001b[0m     config \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig):\n\u001b[0;32m--> 139\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:866\u001b[0m, in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, score_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    864\u001b[0m         estimator\u001b[38;5;241m.\u001b[39mfit(X_train, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[1;32m    865\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 866\u001b[0m         \u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Note fit time as time until error\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     fit_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m start_time\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/core.py:729\u001b[0m, in \u001b[0;36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    727\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args):\n\u001b[1;32m    728\u001b[0m     kwargs[k] \u001b[38;5;241m=\u001b[39m arg\n\u001b[0;32m--> 729\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/sklearn.py:1682\u001b[0m, in \u001b[0;36mXGBClassifier.fit\u001b[0;34m(self, X, y, sample_weight, base_margin, eval_set, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights)\u001b[0m\n\u001b[1;32m   1660\u001b[0m model, metric, params, feature_weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_configure_fit(\n\u001b[1;32m   1661\u001b[0m     xgb_model, params, feature_weights\n\u001b[1;32m   1662\u001b[0m )\n\u001b[1;32m   1663\u001b[0m train_dmatrix, evals \u001b[38;5;241m=\u001b[39m _wrap_evaluation_matrices(\n\u001b[1;32m   1664\u001b[0m     missing\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmissing,\n\u001b[1;32m   1665\u001b[0m     X\u001b[38;5;241m=\u001b[39mX,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1679\u001b[0m     feature_types\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature_types,\n\u001b[1;32m   1680\u001b[0m )\n\u001b[0;32m-> 1682\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_Booster \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1683\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1684\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_dmatrix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1685\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_num_boosting_rounds\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1686\u001b[0m \u001b[43m    \u001b[49m\u001b[43mevals\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1687\u001b[0m \u001b[43m    \u001b[49m\u001b[43mearly_stopping_rounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mearly_stopping_rounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1688\u001b[0m \u001b[43m    \u001b[49m\u001b[43mevals_result\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevals_result\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1689\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1690\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcustom_metric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1691\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1692\u001b[0m \u001b[43m    \u001b[49m\u001b[43mxgb_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1693\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1694\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1696\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobjective):\n\u001b[1;32m   1697\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobjective \u001b[38;5;241m=\u001b[39m params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobjective\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/core.py:729\u001b[0m, in \u001b[0;36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    727\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args):\n\u001b[1;32m    728\u001b[0m     kwargs[k] \u001b[38;5;241m=\u001b[39m arg\n\u001b[0;32m--> 729\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, custom_metric)\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cb_container\u001b[38;5;241m.\u001b[39mbefore_iteration(bst, i, dtrain, evals):\n\u001b[1;32m    182\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m--> 183\u001b[0m \u001b[43mbst\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miteration\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cb_container\u001b[38;5;241m.\u001b[39mafter_iteration(bst, i, dtrain, evals):\n\u001b[1;32m    185\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/core.py:2247\u001b[0m, in \u001b[0;36mBooster.update\u001b[0;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[1;32m   2243\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_assign_dmatrix_features(dtrain)\n\u001b[1;32m   2245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fobj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   2246\u001b[0m     _check_call(\n\u001b[0;32m-> 2247\u001b[0m         \u001b[43m_LIB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mXGBoosterUpdateOneIter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2248\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mctypes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mc_int\u001b[49m\u001b[43m(\u001b[49m\u001b[43miteration\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtrain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle\u001b[49m\n\u001b[1;32m   2249\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2250\u001b[0m     )\n\u001b[1;32m   2251\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2252\u001b[0m     pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict(dtrain, output_margin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "model_xgb = XGBClassifier(\n",
+    "    n_estimators = 200,\n",
+    "    learning_rate = 0.1,\n",
+    "    max_depth = 13,\n",
+    "    subsample = 0.8,\n",
+    "    colsample_bytree = 1.0,\n",
+    "    random_state = 42,\n",
+    "    eval_metric='logloss'\n",
+    "    )\n",
+    "model_xgb.fit(X_train, y_train)\n",
+    "\n",
+    "\n",
+    "from sklearn.metrics import (\n",
+    "    accuracy_score, precision_score, recall_score, f1_score,\n",
+    "    confusion_matrix, classification_report\n",
+    ")\n",
+    "\n",
+    "# Predictions\n",
+    "y_pred = model_xgb.predict(X_test)\n",
+    "\n",
+    "# Use 'macro', 'micro', or 'weighted' depending on what you want:\n",
+    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
+    "print(\"Precision:\", precision_score(y_test, y_pred, average='macro'))\n",
+    "print(\"Recall:\", recall_score(y_test, y_pred, average='macro'))\n",
+    "print(\"F1 Score:\", f1_score(y_test, y_pred, average='macro'))\n",
+    "\n",
+    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred, labels=le.classes_))\n",
+    "\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Confusion Matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, target_names=le.classes_)\n",
+    "disp.plot(cmap=plt.cm.Oranges)\n",
+    "plt.title(\"XGBoost Confusion Matrix\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Feature Importance\n",
+    "importances = model_xgb.feature_importances_\n",
+    "features = [f\"Feature {i}\" for i in range(X_train.shape[1])]\n",
+    "plt.barh(features, importances)\n",
+    "plt.xlabel(\"Importance\")\n",
+    "plt.title(\"Feature Importance\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Cross Validation\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "scores = cross_val_score(model_xgb, X_train, y_train, cv=5, scoring=None, n_jobs=-1)\n",
+    "print(\"Cross-validated AUC scores:\", scores)\n",
+    "\n",
+    "# Learning Curve\n",
+    "from sklearn.model_selection import learning_curve\n",
+    "\n",
+    "train_sizes, train_scores, test_scores = learning_curve(\n",
+    "    model_xgb, X_train, y_train, cv=5, scoring='accuracy', n_jobs=-1\n",
+    ")\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "train_mean = np.mean(train_scores, axis=1)\n",
+    "train_std = np.std(train_scores, axis=1)\n",
+    "test_mean = np.mean(test_scores, axis=1)\n",
+    "test_std = np.std(test_scores, axis=1)\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.plot(train_sizes, train_mean, label=\"Training score\")\n",
+    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.2)\n",
+    "plt.plot(train_sizes, test_mean, label=\"Cross-validation score\")\n",
+    "plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, alpha=0.2)\n",
+    "plt.xlabel(\"Training Set Size\")\n",
+    "plt.ylabel(\"Accuracy\")\n",
+    "plt.title(\"Learning Curve\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Random Regression Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 384 candidates, totalling 1920 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
+      "  warnings.warn(\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:528: FitFailedWarning: \n",
+      "960 fits failed out of a total of 1920.\n",
+      "The score on these train-test partitions for these parameters will be set to nan.\n",
+      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
+      "\n",
+      "Below are more details about the failures:\n",
+      "--------------------------------------------------------------------------------\n",
+      "200 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/base.py\", line 1382, in wrapper\n",
+      "    estimator._validate_params()\n",
+      "  File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/base.py\", line 436, in _validate_params\n",
+      "    validate_parameter_constraints(\n",
+      "  File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/utils/_param_validation.py\", line 98, in validate_parameter_constraints\n",
+      "    raise InvalidParameterError(\n",
+      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "760 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 866, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/base.py\", line 1382, in wrapper\n",
+      "    estimator._validate_params()\n",
+      "  File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/base.py\", line 436, in _validate_params\n",
+      "    validate_parameter_constraints(\n",
+      "  File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/utils/_param_validation.py\", line 98, in validate_parameter_constraints\n",
+      "    raise InvalidParameterError(\n",
+      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
+      "\n",
+      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1108: UserWarning: One or more of the test scores are non-finite: [         nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan -23.75171247\n",
+      " -23.50880275 -23.39548075 -23.33077457 -23.81298832 -23.67713595\n",
+      " -23.46288325 -23.47147848 -24.07421502 -23.90984529 -23.82510818\n",
+      " -23.73108851 -24.62165389 -24.41042527 -24.38709211 -24.33721\n",
+      " -23.92290885 -23.68518437 -23.54005043 -23.50877738 -23.91623042\n",
+      " -23.66143544 -23.58101219 -23.54846533 -24.0604891  -23.92920323\n",
+      " -23.85496765 -23.85781224 -24.6354553  -24.45172809 -24.4204952\n",
+      " -24.386575   -24.24696802 -24.00954424 -23.95911663 -23.91009264\n",
+      " -24.17950152 -24.03350503 -23.96706159 -23.92418141 -24.3895309\n",
+      " -24.09662604 -24.0613816  -24.04088505 -24.71583402 -24.60253384\n",
+      " -24.56825157 -24.5266339  -24.91458214 -24.74402729 -24.70385474\n",
+      " -24.66117498 -24.86650674 -24.74877373 -24.72467115 -24.65693303\n",
+      " -24.87387768 -24.7738635  -24.66532588 -24.65814895 -25.02527487\n",
+      " -24.90141846 -24.81739774 -24.79738643          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan -27.19415405 -27.15435243 -27.1482831\n",
+      " -27.12721617 -27.24648071 -27.17453746 -27.15124104 -27.138674\n",
+      " -27.27146547 -27.23655643 -27.19535799 -27.19233324 -27.34208765\n",
+      " -27.30995177 -27.26716701 -27.28664084 -27.27161464 -27.16096056\n",
+      " -27.16840456 -27.16754828 -27.25347363 -27.17809282 -27.15545757\n",
+      " -27.1538418  -27.32711864 -27.24550414 -27.19461122 -27.18880646\n",
+      " -27.36285429 -27.30710044 -27.30156224 -27.28972018 -27.26277002\n",
+      " -27.21438373 -27.18725251 -27.18469829 -27.26149538 -27.20783247\n",
+      " -27.21529898 -27.17808611 -27.29868576 -27.20708392 -27.19781806\n",
+      " -27.20963306 -27.3389503  -27.33660629 -27.29852057 -27.28709308\n",
+      " -27.3916228  -27.30725787 -27.26291628 -27.27360297 -27.36799444\n",
+      " -27.32305077 -27.3185909  -27.30289303 -27.39941708 -27.34003856\n",
+      " -27.28131454 -27.30343315 -27.34273061 -27.35515768 -27.33329507\n",
+      " -27.31110026          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      " -24.19944851 -24.03859056 -23.95052808 -23.89710044 -24.27320958\n",
+      " -24.16573257 -24.0576949  -23.99167201 -24.53362397 -24.38659547\n",
+      " -24.31691922 -24.23388406 -24.97302004 -24.84808099 -24.75350094\n",
+      " -24.73088255 -24.2837032  -24.11986452 -24.02407527 -24.01506326\n",
+      " -24.23383709 -24.16731224 -24.0657583  -24.06434807 -24.47500863\n",
+      " -24.37939552 -24.33534715 -24.27383962 -24.9727798  -24.85587631\n",
+      " -24.76748671 -24.72731899 -24.56596141 -24.41828246 -24.36217432\n",
+      " -24.31099553 -24.55458616 -24.44713197 -24.36325266 -24.303194\n",
+      " -24.6670522  -24.45914543 -24.44940446 -24.42869783 -25.07543891\n",
+      " -24.89622606 -24.84949668 -24.85366969 -25.11848492 -24.95795668\n",
+      " -24.91581541 -24.92043817 -25.10402041 -25.00402883 -24.9139528\n",
+      " -24.89785092 -25.05178795 -25.00392025 -24.89453664 -24.91539265\n",
+      " -25.25148659 -25.09468585 -25.03904342 -25.02609307]\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters found:  {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 300}\n",
+      "Test MSE: 22.89\n",
+      "Test R^2 Score: 0.31\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Classification metrics can't handle a mix of multiclass and continuous targets",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[17], line 33\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;66;03m# Confusion Matrix\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m cm \u001b[38;5;241m=\u001b[39m \u001b[43mconfusion_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     34\u001b[0m disp \u001b[38;5;241m=\u001b[39m ConfusionMatrixDisplay(confusion_matrix\u001b[38;5;241m=\u001b[39mcm, display_labels\u001b[38;5;241m=\u001b[39mle\u001b[38;5;241m.\u001b[39mclasses_)\n\u001b[1;32m     35\u001b[0m disp\u001b[38;5;241m.\u001b[39mplot(cmap\u001b[38;5;241m=\u001b[39mplt\u001b[38;5;241m.\u001b[39mcm\u001b[38;5;241m.\u001b[39mGreens)\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:216\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    211\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m    212\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    213\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    214\u001b[0m         )\n\u001b[1;32m    215\u001b[0m     ):\n\u001b[0;32m--> 216\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[1;32m    221\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[1;32m    222\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[1;32m    223\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    224\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    225\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m    226\u001b[0m     )\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:340\u001b[0m, in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m    257\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Compute confusion matrix to evaluate the accuracy of a classification.\u001b[39;00m\n\u001b[1;32m    258\u001b[0m \n\u001b[1;32m    259\u001b[0m \u001b[38;5;124;03mBy definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    337\u001b[0m \u001b[38;5;124;03m(np.int64(0), np.int64(2), np.int64(1), np.int64(1))\u001b[39;00m\n\u001b[1;32m    338\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    339\u001b[0m y_true, y_pred \u001b[38;5;241m=\u001b[39m attach_unique(y_true, y_pred)\n\u001b[0;32m--> 340\u001b[0m y_type, y_true, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43m_check_targets\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    341\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    342\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not supported\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m y_type)\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/sklearn/metrics/_classification.py:107\u001b[0m, in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m    104\u001b[0m     y_type \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m    106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(y_type) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 107\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    108\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClassification metrics can\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt handle a mix of \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m targets\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m    109\u001b[0m             type_true, type_pred\n\u001b[1;32m    110\u001b[0m         )\n\u001b[1;32m    111\u001b[0m     )\n\u001b[1;32m    113\u001b[0m \u001b[38;5;66;03m# We can't have more than one value on y_type => The set is no more needed\u001b[39;00m\n\u001b[1;32m    114\u001b[0m y_type \u001b[38;5;241m=\u001b[39m y_type\u001b[38;5;241m.\u001b[39mpop()\n",
+      "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of multiclass and continuous targets"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "param_grid = {\n",
+    "    'n_estimators': [50, 100, 200, 300],\n",
+    "    'max_depth': [None, 10, 20],\n",
+    "    'min_samples_split': [2, 5, 10, 20],\n",
+    "    'min_samples_leaf': [1, 2, 4, 8],\n",
+    "    'max_features': ['log2', 'sqrt']\n",
+    "}\n",
+    "\n",
+    "rf = RandomForestRegressor()\n",
+    "grid_search = GridSearchCV(rf, param_grid, cv=5, scoring='neg_mean_squared_error', n_jobs=-1, verbose=1)\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Best parameters found: \", grid_search.best_params_)\n",
+    "\n",
+    "best_rf = grid_search.best_estimator_\n",
+    "y_pred = best_rf.predict(X_test)\n",
+    "\n",
+    "# Evaluation metrics\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "print(f\"Test MSE: {mse:.2f}\")\n",
+    "print(f\"Test R^2 Score: {r2:.2f}\")\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Confusion Matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n",
+    "disp.plot(cmap=plt.cm.Greens)\n",
+    "plt.title(\"Random Forest Regression Matrix\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Random Forest Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 512 candidates, totalling 2560 fits\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  19.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  36.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  37.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  37.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  37.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  37.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  17.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  54.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  18.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  57.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  53.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  54.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  17.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  54.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  18.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  18.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  35.6s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  35.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  35.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  35.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  35.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.3min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  53.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  17.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  16.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  54.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  52.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  16.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  17.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  17.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  53.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  53.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  33.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  34.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  33.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  33.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  33.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  51.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  50.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  16.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  51.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  50.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  51.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  32.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  32.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  31.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  31.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  31.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  16.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  17.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  47.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  48.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  48.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  17.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  17.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  47.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  47.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  17.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  34.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  34.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  35.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  34.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  34.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  17.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  16.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  51.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  51.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  17.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  17.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  16.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  51.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  51.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  51.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  16.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  17.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  50.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  52.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  16.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  16.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  16.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  51.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  51.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  51.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  33.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  33.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  32.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  34.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  33.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  49.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  49.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  49.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  50.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  50.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  31.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  30.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  31.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  30.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  31.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  16.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  46.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  47.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  15.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  47.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  16.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  45.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  47.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  31.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  31.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  31.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  32.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  31.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  47.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  48.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  16.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  47.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  48.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  48.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  32.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  31.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  33.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  31.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  31.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  15.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  49.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  48.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  15.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  15.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  49.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  16.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  47.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  48.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  31.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  30.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  31.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  32.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  32.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  14.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  15.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  47.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  47.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  14.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  15.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  15.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  47.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  46.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  46.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  29.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  29.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  30.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  30.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  31.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  29.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  58.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  29.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  29.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  60.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  28.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  28.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  42.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  44.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  42.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  42.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  43.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  28.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  29.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  29.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  29.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  28.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  43.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  42.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  15.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  44.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  15.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  43.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  44.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  29.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  57.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  58.1s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  57.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  57.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  57.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  29.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  29.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  29.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  28.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  13.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  14.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  44.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  43.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  14.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  14.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  44.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  43.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  44.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  15.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  27.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  59.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  58.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  59.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  59.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  58.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  28.6s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  29.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  28.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  29.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  14.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  43.0s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  43.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  15.5s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  42.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  15.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  15.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  43.2s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  43.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  29.8s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  57.3s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  56.9s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  58.7s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  57.4s\n",
+      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  58.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  30.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  31.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  30.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  31.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  14.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  15.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  47.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  46.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  14.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  45.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  14.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  45.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  14.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  46.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  29.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  59.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  30.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  29.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  30.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  29.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  45.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  43.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  14.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  14.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  44.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  44.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  45.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  28.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  57.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  59.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  28.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  28.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  59.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  27.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  59.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  28.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  12.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  13.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  41.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  41.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  13.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  42.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  42.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  41.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  25.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  25.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  56.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  56.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  56.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  27.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  55.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  26.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  56.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  26.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  14.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  14.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  39.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  38.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  39.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  39.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  39.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  28.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  51.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  51.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  53.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  52.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  53.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  29.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  28.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  28.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  29.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  14.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  14.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  43.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  43.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  14.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  13.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  43.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  42.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  44.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  56.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  57.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  58.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  28.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  57.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  28.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  57.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  27.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  28.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  28.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  41.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  44.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  42.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  42.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  42.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  27.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  57.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  55.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  56.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  57.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  26.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  26.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  26.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  57.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  28.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  13.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  13.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  40.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  41.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  41.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  40.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  40.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  25.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  25.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  55.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  55.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  54.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  54.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  54.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  25.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  25.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  26.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  13.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  37.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  13.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  38.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  38.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  38.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  39.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  26.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  50.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  50.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  51.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  52.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  51.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  25.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  26.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  26.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  26.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  12.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  38.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  39.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  39.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  13.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  40.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  39.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  25.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  52.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  52.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  51.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  53.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  53.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  13.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  12.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  39.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  39.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  13.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  12.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  39.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  39.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  40.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  25.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  51.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  51.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  53.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  52.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  52.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  26.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  25.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  26.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  26.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  38.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  39.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  39.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  39.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  39.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  25.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  50.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  52.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  52.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  50.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  50.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  25.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  24.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  24.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  25.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  37.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  37.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  12.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  36.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  38.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  38.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  24.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  48.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  49.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  48.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  47.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  49.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  22.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  24.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  23.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  24.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  36.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  35.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  35.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  12.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  12.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  35.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  35.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  46.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  46.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  46.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  47.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  47.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  24.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  24.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  24.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  12.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  36.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  47.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  47.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  47.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  47.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  47.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  24.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  24.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  34.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  46.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  47.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  47.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  47.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  48.1s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  11.9s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  35.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  34.8s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  34.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  12.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  12.2s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  34.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  36.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  25.4s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  46.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  46.7s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  46.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  46.3s\n",
+      "[CV] END max_depth=None, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  47.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  12.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  37.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  35.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  11.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  37.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  36.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  35.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  47.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  47.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  48.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  48.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  48.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  24.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  24.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  24.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  11.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  12.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  36.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  36.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  36.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  12.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  35.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  12.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  36.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  47.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  47.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  48.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  49.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  47.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  24.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  12.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  11.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  36.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  36.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  11.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  36.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  36.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  35.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  47.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  48.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  47.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  47.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  48.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  24.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  23.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  24.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  11.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  35.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  35.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  12.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  36.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  35.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  35.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  48.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  46.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  48.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  49.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  48.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  24.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  11.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  35.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  12.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  35.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  35.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  36.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  36.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  46.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  46.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  48.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  49.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  49.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  24.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  24.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  24.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  35.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  35.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  12.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  36.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  35.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  36.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  23.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  47.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  49.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  47.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  47.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  48.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  24.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  34.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  36.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  36.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  23.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  47.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  47.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  47.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  47.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  47.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  23.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  35.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  35.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  35.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  34.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  36.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  46.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  47.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  48.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  48.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  47.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  24.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  12.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  36.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  12.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  35.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  12.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  35.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  35.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  47.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  46.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  47.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  48.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  48.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  24.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  35.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  36.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  11.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  34.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  35.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  23.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  47.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  47.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  47.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  48.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  46.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  23.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  24.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  11.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  35.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  35.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  11.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  35.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  35.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  47.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  47.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  47.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  47.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  47.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  23.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  23.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  36.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  34.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  35.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  35.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  23.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  46.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  46.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  46.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  23.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  23.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  47.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  23.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  48.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  34.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  34.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  35.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  34.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  34.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  24.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  47.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  46.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  47.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  47.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  47.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  33.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  34.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  45.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  47.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  46.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  48.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  22.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  47.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  34.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  34.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  34.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  34.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  46.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  46.0s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  47.8s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  46.9s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  46.5s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   9.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  10.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  34.4s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  34.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  10.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  10.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  34.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  35.2s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  35.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  19.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  20.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  45.6s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  46.3s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  46.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  19.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  20.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  19.1s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  46.7s\n",
+      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  47.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  29.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  10.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   9.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  29.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  29.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   9.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  10.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   9.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  28.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  30.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  19.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  39.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  39.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  39.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  40.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  19.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  38.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  20.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  20.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   9.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   9.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  29.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  29.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  10.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  10.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  30.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  28.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  29.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  39.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  20.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  38.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  38.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  39.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  40.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  19.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  19.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  20.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   9.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  29.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  29.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  10.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  29.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  29.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  29.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  39.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  39.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  39.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  39.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  39.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  19.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  18.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  19.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  19.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   9.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  28.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  28.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  29.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   9.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  29.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  29.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  19.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  38.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  38.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  38.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  38.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  39.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  19.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  20.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   9.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  28.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  10.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  29.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  10.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  29.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   9.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  28.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  29.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  20.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  40.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  39.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  40.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  38.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  38.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  19.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  19.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  19.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  19.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   9.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   9.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  29.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  29.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   9.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   9.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  30.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  30.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  30.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  19.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  38.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  39.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  39.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  38.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  38.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  19.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  19.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  20.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  29.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  30.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   9.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  30.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  29.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  29.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  10.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  38.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  38.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  38.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  19.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  38.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  38.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  19.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  20.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   9.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  28.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   9.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  29.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  28.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  29.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   9.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  28.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  38.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  19.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  39.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  39.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  38.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  18.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  40.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  19.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  19.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  19.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   9.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   9.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  28.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  28.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  10.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  28.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  10.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  29.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  29.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  19.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  39.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  38.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  38.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  37.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  38.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  19.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  19.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  19.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  10.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   9.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  29.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  29.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  29.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   9.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   9.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  10.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  29.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  29.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  19.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  39.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  38.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  38.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  38.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  38.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  19.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  19.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  19.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  21.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   9.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  10.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  29.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  28.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   9.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  28.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  10.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  29.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  29.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  38.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  37.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  38.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  38.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  38.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  19.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  19.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  19.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  29.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  28.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   9.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  10.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  28.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  28.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  29.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  19.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  37.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  39.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  39.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  38.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  38.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  18.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  19.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  18.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  28.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  27.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   9.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   9.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=   9.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  28.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  28.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  28.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  19.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  38.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  38.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  38.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  38.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  19.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  39.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  18.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  19.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  19.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   9.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   9.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  28.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  28.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  27.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  10.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=   9.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  28.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  28.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  18.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  37.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  38.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  38.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  39.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  18.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  38.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  18.7s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  19.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  18.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   9.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   9.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  28.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  28.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   9.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=   9.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  10.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  28.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  28.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  28.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  18.4s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  37.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  37.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  38.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  37.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  38.9s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  19.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  19.6s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  18.3s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  19.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  28.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  28.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  28.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  28.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  28.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.1s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  37.0s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  37.8s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  37.2s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  39.5s\n",
+      "[CV] END max_depth=10, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  37.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  17.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  35.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  36.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  35.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  35.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  36.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  16.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  17.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  54.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  53.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  53.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  54.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  54.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  17.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  17.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  17.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  34.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  34.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  34.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  34.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  34.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  17.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  17.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  52.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  53.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  51.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  51.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  17.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  16.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  16.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  51.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  34.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  32.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  34.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  34.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  33.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  49.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  51.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  51.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  50.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  51.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  31.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  31.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  31.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  32.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  32.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  17.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  16.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  47.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  46.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  16.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  47.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  16.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  16.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  46.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  48.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  34.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  34.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  33.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  33.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  34.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  16.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  50.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  16.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  52.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  16.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  49.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  50.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  17.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  17.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  51.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  33.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  33.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  15.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  16.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  50.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  50.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  16.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  16.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  16.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  50.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  50.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  51.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  33.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  32.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  32.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  32.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  32.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  16.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  49.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  49.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  16.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  48.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  49.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  48.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  30.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  30.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  30.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  31.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  31.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  15.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  15.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  45.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  46.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  16.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  45.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  15.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  15.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  47.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  47.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  31.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  31.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  30.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  32.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  31.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  47.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  48.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  46.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  46.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  47.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  32.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  31.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  31.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  30.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  31.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  15.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  16.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  47.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  46.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  15.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  16.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  47.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  48.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  47.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  31.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  31.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  32.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  31.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  31.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  14.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  14.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  47.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  47.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  15.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  46.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  15.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  14.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  45.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  47.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  30.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  28.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  30.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  30.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  30.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  46.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  46.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  28.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  58.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  59.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  29.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  59.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  29.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  28.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  29.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  43.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  43.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  43.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  43.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  42.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  30.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  56.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  29.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  27.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  29.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  28.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  42.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  41.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  43.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  43.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  43.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  29.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  56.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  56.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  57.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  58.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  29.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  28.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  58.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  28.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  28.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  13.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  44.3s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  42.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  43.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  13.9s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  14.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  42.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  43.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  28.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  56.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  57.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  57.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  57.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  58.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  28.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  28.7s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  29.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  29.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  15.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  42.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  14.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  42.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  14.6s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  42.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  15.2s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  42.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  42.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  14.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  29.1s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  56.8s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  57.5s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  58.0s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  57.4s\n",
+      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  57.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  31.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  29.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  29.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  30.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  14.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  43.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  43.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  15.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  13.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  13.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  14.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  44.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  45.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  45.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  28.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  58.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  59.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  59.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  28.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  28.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  28.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time=  59.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  29.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  41.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  43.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  43.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  43.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  42.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  27.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  57.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  28.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  27.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  56.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  57.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  57.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  58.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  27.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  27.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  12.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  12.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  40.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  41.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  13.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  12.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  41.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  41.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  41.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  26.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  53.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  25.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  26.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  54.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  54.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  55.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  55.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  25.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  25.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  13.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  13.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  38.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  39.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  13.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  13.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  39.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  38.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  38.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  14.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  51.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  51.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  52.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  50.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  52.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  28.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  27.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  28.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  28.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  28.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  13.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  42.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  41.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  13.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  41.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  41.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  41.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  13.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  14.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  56.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  27.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  57.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  56.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  55.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  56.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  28.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  27.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  27.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  27.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  40.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  42.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  41.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  41.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  42.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  27.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  55.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  54.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  55.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  56.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  26.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  27.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  27.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  56.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  26.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  13.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  40.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  41.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  13.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  41.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  41.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  39.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  25.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  53.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  53.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  24.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  53.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  53.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  53.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  25.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  25.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  26.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  12.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  12.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  37.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  38.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  39.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  13.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  37.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  38.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  26.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  51.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  50.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  49.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  50.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  50.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  25.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  26.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  26.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  25.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  38.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  38.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  13.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  12.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  40.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  38.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  38.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  52.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  52.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  51.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  52.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  53.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  25.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  25.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  39.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  39.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  13.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  12.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  37.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  38.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  40.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  26.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  51.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  51.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  52.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  51.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  51.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  26.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  25.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  26.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  25.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  11.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  39.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  38.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  39.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  38.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  38.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  24.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  51.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  51.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  25.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  51.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  51.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  51.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  25.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  24.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  24.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  36.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  36.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  37.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  36.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  37.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  24.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  48.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  49.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  48.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  23.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  49.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  24.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  24.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  50.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  36.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  36.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  35.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  35.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  46.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  47.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  46.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  48.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  49.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  22.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  34.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  36.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  46.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  46.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  48.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  49.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  48.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  24.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  34.7s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.9s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.0s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  48.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  45.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  47.1s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  47.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  47.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  22.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  24.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  33.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  36.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  17.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.5s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  34.6s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  33.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  35.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  18.8s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  46.3s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  47.2s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  46.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  46.4s\n",
+      "[CV] END max_depth=20, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  47.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  37.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  37.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  36.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  36.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  38.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  17.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  17.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  56.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  55.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  54.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  55.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  55.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  18.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  17.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  17.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  35.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  35.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.3min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  36.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  36.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  35.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  16.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  17.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  53.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  52.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  52.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  17.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  17.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  54.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  17.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  53.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  34.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  34.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  35.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  34.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  34.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  16.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  52.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  51.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  16.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  15.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  50.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  51.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  51.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  32.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  32.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  31.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  31.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  31.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  17.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  17.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  48.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  48.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  17.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  47.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  17.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  17.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  48.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  48.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  35.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  34.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  33.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  34.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  35.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  17.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  16.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  52.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  53.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  17.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  51.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  51.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  17.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  17.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  52.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  33.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  34.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  16.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  50.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  51.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  16.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  17.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  16.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  51.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  51.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  52.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  33.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.2min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  32.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  33.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  33.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  32.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  16.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  50.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  49.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  15.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  50.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  49.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  50.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  31.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  32.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  30.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  32.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  32.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  15.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  15.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  46.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  46.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  46.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  16.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  16.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  46.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  47.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  32.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  31.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  32.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  32.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  32.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  16.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  47.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  47.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  16.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  15.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  47.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  46.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  48.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  31.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  31.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  33.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  32.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  32.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  15.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  16.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  47.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  47.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  15.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  46.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  15.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  15.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  47.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  47.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  31.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  30.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  31.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  32.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  31.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  14.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  14.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  47.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  47.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  14.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  15.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  15.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  47.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  46.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  47.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  29.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  29.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  30.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time= 1.1min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  29.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  31.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  45.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  46.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  28.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  59.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  27.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  59.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  28.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  29.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  29.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  43.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  42.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  42.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  43.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  43.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  28.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  58.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  59.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  57.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  29.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  28.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  28.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  29.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  42.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  43.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  14.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  43.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  44.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  42.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  28.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  57.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  57.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  58.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  56.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  56.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  28.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  28.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  29.5s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  29.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  14.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  43.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  42.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  14.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  44.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  14.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  15.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  43.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  45.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  27.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  56.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  57.3s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  56.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  56.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  57.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  28.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  28.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  28.2s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  28.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  15.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  15.1s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  43.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  42.7s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  43.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  14.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  15.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  41.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  15.0s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  42.9s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  56.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  30.4s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  56.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  57.6s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  56.8s\n",
+      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  57.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  29.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  30.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  29.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  30.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  14.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  45.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  45.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  14.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  14.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  14.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  45.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  46.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  46.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  28.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  29.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  30.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  30.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  29.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  14.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  44.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  44.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  44.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  14.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  44.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  44.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  28.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  58.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  58.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  27.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  59.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time= 1.0min\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  28.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=400; total time=  58.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  28.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  28.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  13.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  13.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  43.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  43.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  13.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  13.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  41.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  40.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  41.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  26.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  25.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  55.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  55.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  26.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  26.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  55.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  57.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=400; total time=  57.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=200; total time=  25.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  14.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  39.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  39.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  14.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  39.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  13.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=  14.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  38.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=300; total time=  39.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  51.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  28.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  52.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  53.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  53.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=20, n_estimators=400; total time=  52.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  29.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  28.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  28.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  29.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  14.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  42.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  14.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  42.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=  14.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  42.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  44.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  43.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  28.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  56.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  57.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  57.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  57.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=400; total time=  58.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  28.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  28.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  28.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  28.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  41.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  43.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  14.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  41.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=  13.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  41.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  42.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  27.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  57.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  55.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  57.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  27.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  26.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  27.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  56.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  27.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=400; total time=  59.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  39.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  40.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=100; total time=  12.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  40.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  41.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  42.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  25.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  53.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  25.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  25.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  53.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  55.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  54.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=400; total time=  54.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  26.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=200; total time=  25.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  13.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  13.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  38.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  38.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  38.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  12.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  13.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=  14.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  38.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=300; total time=  39.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  50.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  50.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  25.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  51.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  51.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  25.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  26.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=20, n_estimators=400; total time=  52.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  26.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  27.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  13.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  13.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  39.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  39.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  12.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  39.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=  13.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  39.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  40.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  51.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  51.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  52.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  53.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=400; total time=  55.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=  26.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  12.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  12.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  38.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  39.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  13.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  13.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=  13.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  38.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  39.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  39.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  25.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  51.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  53.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  52.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  52.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=400; total time=  53.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  26.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  26.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  26.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=  25.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  38.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  38.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  39.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  39.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  38.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  25.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  52.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  53.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  24.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  24.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  52.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  24.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  51.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=400; total time=  52.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=200; total time=  25.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  36.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  38.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  37.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  37.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=300; total time=  37.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  24.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  48.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  48.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  49.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  48.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=20, n_estimators=400; total time=  50.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  23.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  24.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=200; total time=  24.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  35.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  35.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  12.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=100; total time=  11.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  37.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  34.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=300; total time=  34.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  45.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  47.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  47.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  48.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=2, n_estimators=400; total time=  48.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  24.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=200; total time=  24.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  34.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  12.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=100; total time=  11.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  36.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=300; total time=  35.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  46.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  47.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  47.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  49.2s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=5, n_estimators=400; total time=  49.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  24.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  23.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=200; total time=  26.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  36.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  12.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  11.7s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=100; total time=  12.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=300; total time=  35.3s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  22.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  46.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  47.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  48.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  47.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=10, n_estimators=400; total time=  48.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  24.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  24.6s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  23.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=200; total time=  24.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  34.4s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  34.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  32.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  32.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=300; total time=  32.5s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  40.8s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  41.0s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  41.9s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  41.1s\n",
+      "[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=400; total time=  39.4s\n",
+      "Best parameters found:  {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
+      "Test Accuracy: 0.54\n",
+      "\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.86      0.30      0.45       186\n",
+      "           1       0.82      0.82      0.82       170\n",
+      "           2       0.89      0.38      0.53       234\n",
+      "           3       0.68      0.26      0.37       170\n",
+      "           4       0.83      0.03      0.06       161\n",
+      "           5       0.54      0.16      0.25       230\n",
+      "           6       0.87      0.47      0.61       165\n",
+      "           7       0.66      0.41      0.51       255\n",
+      "           8       0.56      0.63      0.59       636\n",
+      "           9       0.53      0.33      0.41       198\n",
+      "          10       0.81      0.25      0.38       224\n",
+      "          11       0.66      0.63      0.65       199\n",
+      "          12       0.72      0.85      0.78       188\n",
+      "          13       0.59      0.69      0.63       590\n",
+      "          14       0.79      0.56      0.65       122\n",
+      "          15       0.43      0.59      0.50      1020\n",
+      "          16       1.00      0.10      0.19       105\n",
+      "          17       0.53      0.71      0.61       424\n",
+      "          18       0.43      0.66      0.52       967\n",
+      "          19       0.45      0.28      0.34       163\n",
+      "          20       0.61      0.66      0.63       482\n",
+      "\n",
+      "    accuracy                           0.54      6889\n",
+      "   macro avg       0.68      0.47      0.50      6889\n",
+      "weighted avg       0.60      0.54      0.53      6889\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "\n",
+    "param_grid = {\n",
+    "    'n_estimators': [100, 200, 300, 400],             # Number of trees\n",
+    "    'max_depth': [None, 10, 20, 30],             # Max depth of each tree\n",
+    "    'min_samples_split': [2, 5, 10, 20],             # Minimum samples to split an internal node\n",
+    "    'min_samples_leaf': [1, 2, 4, 8],               # Minimum samples at a leaf node\n",
+    "    'max_features': ['sqrt', 'log2']             # Number of features considered for split\n",
+    "}\n",
+    "\n",
+    "rf = RandomForestClassifier()\n",
+    "grid_search = GridSearchCV(rf, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=2)\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Best parameters found: \", grid_search.best_params_)\n",
+    "\n",
+    "best_rf = grid_search.best_estimator_\n",
+    "y_pred = best_rf.predict(X_test)\n",
+    "\n",
+    "# Accuracy and detailed report\n",
+    "print(f\"Test Accuracy: {accuracy_score(y_test, y_pred):.2f}\")\n",
+    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Confusion Matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n",
+    "disp.plot(cmap=plt.cm.Greens)\n",
+    "plt.title(\"Random Forest Confusion Matrix\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluation Charts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Confusion Matrix - Shows what was the predicted value compared to the true value when evaluating the Model\n",
+    "\n",
+    "The more values in the diagonal line the better"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AxisError",
+     "evalue": "axis 1 is out of bounds for array of dimension 1",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAxisError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[13], line 6\u001b[0m\n\u001b[1;32m      2\u001b[0m BEST_MODEL \u001b[38;5;241m=\u001b[39m best_knn \u001b[38;5;66;03m# KNN\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m#Confusion Matrix\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# Get predictions on the test set.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbest_knn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m decoded_y_test \u001b[38;5;241m=\u001b[39m le\u001b[38;5;241m.\u001b[39minverse_transform(y_test)\n\u001b[1;32m      9\u001b[0m decoded_y_pred \u001b[38;5;241m=\u001b[39m le\u001b[38;5;241m.\u001b[39minverse_transform(y_pred)\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/numpy/core/fromnumeric.py:1229\u001b[0m, in \u001b[0;36margmax\u001b[0;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[1;32m   1142\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1143\u001b[0m \u001b[38;5;124;03mReturns the indices of the maximum values along an axis.\u001b[39;00m\n\u001b[1;32m   1144\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1226\u001b[0m \u001b[38;5;124;03m(2, 1, 4)\u001b[39;00m\n\u001b[1;32m   1227\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1228\u001b[0m kwds \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkeepdims\u001b[39m\u001b[38;5;124m'\u001b[39m: keepdims} \u001b[38;5;28;01mif\u001b[39;00m keepdims \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39m_NoValue \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[0;32m-> 1229\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43margmax\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/numpy/core/fromnumeric.py:59\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _wrapit(obj, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbound\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;66;03m# A TypeError occurs if the object does have such a method in its\u001b[39;00m\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;66;03m# class, but its signature is not identical to that of NumPy's. This\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[38;5;66;03m# Call _wrapit from within the except clause to ensure a potential\u001b[39;00m\n\u001b[1;32m     67\u001b[0m     \u001b[38;5;66;03m# exception has a traceback chain.\u001b[39;00m\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _wrapit(obj, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n",
+      "\u001b[0;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1"
+     ]
+    }
+   ],
+   "source": [
+    "#BEST_MODEL = best_model # ANN\n",
+    "#BEST_MODEL = best_knn # KNN\n",
+    "\n",
+    "#Confusion Matrix\n",
+    "# Get predictions on the test set.\n",
+    "y_pred = np.argmax(best_model.predict(X_test), axis=1)\n",
+    "\n",
+    "decoded_y_test = le.inverse_transform(y_test)\n",
+    "decoded_y_pred = le.inverse_transform(y_pred)\n",
+    "\n",
+    "# Create confusion matrix.\n",
+    "cm = confusion_matrix(decoded_y_test, decoded_y_pred)\n",
+    "\n",
+    "plt.figure(figsize=(8,6))\n",
+    "sns.heatmap(cm, annot=True, fmt='d', cmap='viridis', xticklabels=le.classes_, yticklabels=le.classes_)\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('True')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loss and Accuracy Plots - Shows how the models loss and accuracy values changed during the training\n",
+    "\n",
+    "The closer the lines to each other the better"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot training vs. validation loss and accuracy.\n",
+    "plt.figure(figsize=(12,5))\n",
+    "\n",
+    "# Loss Plot\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(history.history['loss'], label='Training Loss')\n",
+    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Loss')\n",
+    "plt.title('Training vs. Validation Loss')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Accuracy Plot\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
+    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.title('Training vs. Validation Accuracy')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature Corrleation Matrix - How much the features correlate to each other \n",
+    "\n",
+    "Less correlating features is better"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_features = pd.DataFrame(X_train, columns=[f'feature_{i}' for i in range(X_train.shape[1])])\n",
+    "\n",
+    "# Compute the correlation matrix.\n",
+    "corr = df_features.corr()\n",
+    "\n",
+    "plt.figure(figsize=(12,10))\n",
+    "sns.heatmap(corr, cmap='coolwarm', annot=False)\n",
+    "plt.title('Feature Correlation Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature permutation importance - shows how much each feature was important in making a decision in the model\n",
+    "\n",
+    "Potential: remove the non-important features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 667us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 600us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 594us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 583us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 584us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 548us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 610us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 603us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 624us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 616us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 636us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 668us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 581us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 618us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 582us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 591us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 563us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 560us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 635us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 652us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 632us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 666us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 627us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 582us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 576us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 596us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 616us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 615us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 729us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 785us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 667us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 864us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 616us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 610us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 649us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 611us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 596us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 593us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 625us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 644us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 625us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 626us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 661us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 610us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 592us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 659us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 667us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 712us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 556us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 583us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 616us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 630us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 645us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 620us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 603us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 610us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 741us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 660us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 617us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 564us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 604us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 596us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 559us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 571us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 659us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 753us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 611us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 581us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 585us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 646us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 614us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 672us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 589us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 597us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 782us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 626us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 712us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 622us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 615us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 608us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 587us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 587us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 652us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 585us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 629us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 603us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 611us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 624us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 606us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 667us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 615us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 732us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 594us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 577us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 583us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 587us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 581us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 615us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 562us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 603us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 717us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 672us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 681us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 651us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 641us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 613us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 631us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 765us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 629us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 584us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 595us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 988us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 666us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 650us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 638us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 693us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 628us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 613us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 695us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 589us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 580us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 601us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 641us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 645us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 706us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 624us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 654us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 607us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 671us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 619us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 606us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 576us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 633us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 717us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 607us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 587us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 569us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 593us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 578us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 562us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 573us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 640us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 653us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 658us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 614us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 579us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 603us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 605us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 593us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 581us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 605us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 601us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 694us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 626us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 653us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 582us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 562us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 565us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 599us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 617us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 637us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 788us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 672us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 627us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 649us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 607us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 604us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 625us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 591us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 647us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 764us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 616us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 596us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 575us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 594us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 534us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 548us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 701us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 680us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 630us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 573us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 569us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 655us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 588us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 603us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 578us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 594us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 619us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 641us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 579us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 687us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 647us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 600us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 606us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 608us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 637us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 581us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 578us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 597us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 571us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 610us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 627us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 633us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 607us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 621us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 632us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 579us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 628us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 631us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 636us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 623us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 570us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 669us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 600us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 750us/step\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 601us/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.base import BaseEstimator, ClassifierMixin\n",
+    "from sklearn.inspection import permutation_importance\n",
+    "\n",
+    "# Wrapper for the Keras model to work with sklearn's permutation_importance.\n",
+    "class KerasClassifierWrapper(BaseEstimator, ClassifierMixin):\n",
+    "    def __init__(self, model):\n",
+    "        self.model = model\n",
+    "        \n",
+    "    def fit(self, X, y):\n",
+    "        # Model is already trained.\n",
+    "        return self\n",
+    "    \n",
+    "    def predict(self, X):\n",
+    "        preds = self.model.predict(X)\n",
+    "        return np.argmax(preds, axis=1)\n",
+    "    \n",
+    "    def score(self, X, y):\n",
+    "        y_pred = self.predict(X)\n",
+    "        return np.mean(y_pred == y)\n",
+    "\n",
+    "# Wrap our trained model.\n",
+    "wrapper = KerasClassifierWrapper(best_model)\n",
+    "\n",
+    "# Compute permutation importance on the test set.\n",
+    "result = permutation_importance(wrapper, X_test, y_test, n_repeats=10,\n",
+    "                                random_state=42, scoring='accuracy')\n",
+    "importance_means = result.importances_mean\n",
+    "\n",
+    "# Plot the permutation importance.\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "features = [f'feature_{i}' for i in range(X_filtered.shape[1])]\n",
+    "plt.bar(features, importance_means)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.xlabel('Feature')\n",
+    "plt.ylabel('Mean Importance')\n",
+    "plt.title('Permutation Feature Importance')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Remove unimportant features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Important feature indices: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 14 15 16 17 19 20 21]\n",
+      "Important features: ['feature_0', 'feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'feature_6', 'feature_7', 'feature_8', 'feature_9', 'feature_10', 'feature_11', 'feature_12', 'feature_14', 'feature_15', 'feature_16', 'feature_17', 'feature_19', 'feature_20', 'feature_21']\n",
+      "Original training shape: (9152, 22)\n",
+      "Reduced training shape: (9152, 20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Assume importance_means and importance_threshold are already defined\n",
+    "importance_threshold = 0.01  # adjust this threshold as needed\n",
+    "\n",
+    "# Check if X_train is a DataFrame, if not, convert it and assign column names.\n",
+    "if isinstance(X_train, np.ndarray):\n",
+    "    X_train_df = pd.DataFrame(X_train, columns=[f'feature_{i}' for i in range(X_train.shape[1])])\n",
+    "else:\n",
+    "    X_train_df = X_train.copy()\n",
+    "\n",
+    "# Do the same for X_test.\n",
+    "if isinstance(X_test, np.ndarray):\n",
+    "    X_test_df = pd.DataFrame(X_test, columns=[f'feature_{i}' for i in range(X_test.shape[1])])\n",
+    "else:\n",
+    "    X_test_df = X_test.copy()\n",
+    "\n",
+    "# Identify the indices of features with importance greater than or equal to the threshold.\n",
+    "important_feature_indices = np.where(importance_means >= importance_threshold)[0]\n",
+    "print(\"Important feature indices:\", important_feature_indices)\n",
+    "\n",
+    "# Retrieve the names of the important features.\n",
+    "important_features = X_train_df.columns[important_feature_indices]\n",
+    "print(\"Important features:\", important_features.tolist())\n",
+    "\n",
+    "# Filter the training and test DataFrames to keep only the important features.\n",
+    "X_train_reduced = X_train_df[important_features]\n",
+    "X_test_reduced = X_test_df[important_features]\n",
+    "\n",
+    "print(\"Original training shape:\", X_train_df.shape)\n",
+    "print(\"Reduced training shape:\", X_train_reduced.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Re-run Hyperparameter search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 15 Complete [00h 00m 27s]\n",
+      "val_accuracy: 0.5314685106277466\n",
+      "\n",
+      "Best val_accuracy So Far: 0.5375874042510986\n",
+      "Total elapsed time: 00h 05m 30s\n",
+      "Best Hyperparameters:\n",
+      "{'activation_1': 'relu', 'units_1': 32, 'dropout_rate': 0.30000000000000004, 'second_layer': False, 'learning_rate': 0.001}\n",
+      "Epoch 1/60\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.1285 - loss: 3.0461 - val_accuracy: 0.3759 - val_loss: 2.2128\n",
+      "Epoch 2/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.3408 - loss: 2.2574 - val_accuracy: 0.4506 - val_loss: 1.9569\n",
+      "Epoch 3/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.3925 - loss: 2.0439 - val_accuracy: 0.4729 - val_loss: 1.8457\n",
+      "Epoch 4/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4228 - loss: 1.9363 - val_accuracy: 0.4847 - val_loss: 1.7760\n",
+      "Epoch 5/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4382 - loss: 1.8784 - val_accuracy: 0.4978 - val_loss: 1.7353\n",
+      "Epoch 6/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4466 - loss: 1.8364 - val_accuracy: 0.4991 - val_loss: 1.7031\n",
+      "Epoch 7/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4558 - loss: 1.8005 - val_accuracy: 0.5096 - val_loss: 1.6780\n",
+      "Epoch 8/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.4622 - loss: 1.7797 - val_accuracy: 0.5118 - val_loss: 1.6606\n",
+      "Epoch 9/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4604 - loss: 1.7700 - val_accuracy: 0.5149 - val_loss: 1.6474\n",
+      "Epoch 10/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4707 - loss: 1.7512 - val_accuracy: 0.5201 - val_loss: 1.6342\n",
+      "Epoch 11/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4722 - loss: 1.7367 - val_accuracy: 0.5192 - val_loss: 1.6262\n",
+      "Epoch 12/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4804 - loss: 1.7286 - val_accuracy: 0.5188 - val_loss: 1.6177\n",
+      "Epoch 13/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4856 - loss: 1.7098 - val_accuracy: 0.5219 - val_loss: 1.6084\n",
+      "Epoch 14/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4793 - loss: 1.7305 - val_accuracy: 0.5236 - val_loss: 1.6025\n",
+      "Epoch 15/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4800 - loss: 1.7103 - val_accuracy: 0.5253 - val_loss: 1.5976\n",
+      "Epoch 16/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4874 - loss: 1.7044 - val_accuracy: 0.5315 - val_loss: 1.5893\n",
+      "Epoch 17/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4857 - loss: 1.6971 - val_accuracy: 0.5253 - val_loss: 1.5883\n",
+      "Epoch 18/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4893 - loss: 1.6964 - val_accuracy: 0.5288 - val_loss: 1.5838\n",
+      "Epoch 19/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4931 - loss: 1.6822 - val_accuracy: 0.5302 - val_loss: 1.5769\n",
+      "Epoch 20/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4856 - loss: 1.6823 - val_accuracy: 0.5310 - val_loss: 1.5722\n",
+      "Epoch 21/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4905 - loss: 1.6837 - val_accuracy: 0.5284 - val_loss: 1.5704\n",
+      "Epoch 22/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4936 - loss: 1.6710 - val_accuracy: 0.5315 - val_loss: 1.5648\n",
+      "Epoch 23/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4949 - loss: 1.6697 - val_accuracy: 0.5302 - val_loss: 1.5649\n",
+      "Epoch 24/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4950 - loss: 1.6645 - val_accuracy: 0.5328 - val_loss: 1.5615\n",
+      "Epoch 25/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4934 - loss: 1.6713 - val_accuracy: 0.5350 - val_loss: 1.5563\n",
+      "Epoch 26/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4937 - loss: 1.6534 - val_accuracy: 0.5337 - val_loss: 1.5527\n",
+      "Epoch 27/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5005 - loss: 1.6560 - val_accuracy: 0.5358 - val_loss: 1.5502\n",
+      "Epoch 28/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4951 - loss: 1.6478 - val_accuracy: 0.5323 - val_loss: 1.5471\n",
+      "Epoch 29/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.4966 - loss: 1.6530 - val_accuracy: 0.5341 - val_loss: 1.5420\n",
+      "Epoch 30/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.4981 - loss: 1.6464 - val_accuracy: 0.5350 - val_loss: 1.5408\n",
+      "Epoch 31/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.5037 - loss: 1.6419 - val_accuracy: 0.5315 - val_loss: 1.5393\n",
+      "Epoch 32/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4903 - loss: 1.6386 - val_accuracy: 0.5337 - val_loss: 1.5371\n",
+      "Epoch 33/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4996 - loss: 1.6438 - val_accuracy: 0.5323 - val_loss: 1.5364\n",
+      "Epoch 34/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4949 - loss: 1.6318 - val_accuracy: 0.5319 - val_loss: 1.5337\n",
+      "Epoch 35/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5002 - loss: 1.6327 - val_accuracy: 0.5288 - val_loss: 1.5352\n",
+      "Epoch 36/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5037 - loss: 1.6358 - val_accuracy: 0.5328 - val_loss: 1.5303\n",
+      "Epoch 37/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4977 - loss: 1.6366 - val_accuracy: 0.5310 - val_loss: 1.5279\n",
+      "Epoch 38/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4986 - loss: 1.6288 - val_accuracy: 0.5389 - val_loss: 1.5229\n",
+      "Epoch 39/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4989 - loss: 1.6183 - val_accuracy: 0.5332 - val_loss: 1.5227\n",
+      "Epoch 40/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5086 - loss: 1.6188 - val_accuracy: 0.5406 - val_loss: 1.5205\n",
+      "Epoch 41/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.5025 - loss: 1.6280 - val_accuracy: 0.5350 - val_loss: 1.5212\n",
+      "Epoch 42/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5024 - loss: 1.6188 - val_accuracy: 0.5402 - val_loss: 1.5201\n",
+      "Epoch 43/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4987 - loss: 1.6165 - val_accuracy: 0.5328 - val_loss: 1.5195\n",
+      "Epoch 44/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4979 - loss: 1.6114 - val_accuracy: 0.5367 - val_loss: 1.5194\n",
+      "Epoch 45/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5039 - loss: 1.6067 - val_accuracy: 0.5293 - val_loss: 1.5145\n",
+      "Epoch 46/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4994 - loss: 1.6275 - val_accuracy: 0.5363 - val_loss: 1.5134\n",
+      "Epoch 47/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5038 - loss: 1.6156 - val_accuracy: 0.5354 - val_loss: 1.5110\n",
+      "Epoch 48/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5055 - loss: 1.6100 - val_accuracy: 0.5354 - val_loss: 1.5086\n",
+      "Epoch 49/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5058 - loss: 1.6120 - val_accuracy: 0.5441 - val_loss: 1.5093\n",
+      "Epoch 50/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5048 - loss: 1.6017 - val_accuracy: 0.5415 - val_loss: 1.5076\n",
+      "Epoch 51/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5019 - loss: 1.6037 - val_accuracy: 0.5345 - val_loss: 1.5069\n",
+      "Epoch 52/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5035 - loss: 1.6016 - val_accuracy: 0.5437 - val_loss: 1.5046\n",
+      "Epoch 53/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5022 - loss: 1.6106 - val_accuracy: 0.5437 - val_loss: 1.5001\n",
+      "Epoch 54/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5054 - loss: 1.5944 - val_accuracy: 0.5402 - val_loss: 1.5002\n",
+      "Epoch 55/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5021 - loss: 1.6006 - val_accuracy: 0.5315 - val_loss: 1.5013\n",
+      "Epoch 56/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5054 - loss: 1.5938 - val_accuracy: 0.5363 - val_loss: 1.4998\n",
+      "Epoch 57/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5072 - loss: 1.6059 - val_accuracy: 0.5376 - val_loss: 1.4971\n",
+      "Epoch 58/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.5087 - loss: 1.5961 - val_accuracy: 0.5354 - val_loss: 1.4990\n",
+      "Epoch 59/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5021 - loss: 1.5939 - val_accuracy: 0.5354 - val_loss: 1.4950\n",
+      "Epoch 60/60\n",
+      "\u001b[1m286/286\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5031 - loss: 1.6035 - val_accuracy: 0.5323 - val_loss: 1.4978\n",
+      "\u001b[1m72/72\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5464 - loss: 1.5069\n",
+      "Test accuracy: 0.5354\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import keras_tuner as kt\n",
+    "from tensorflow.keras import layers, models, regularizers\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "from sklearn.metrics import confusion_matrix, classification_report\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Ensure reproducibility\n",
+    "np.random.seed(42)\n",
+    "tf.random.set_seed(42)\n",
+    "\n",
+    "# Assuming X_train, X_test, y_train, y_test are already defined\n",
+    "num_features = X_train_reduced.shape[1]\n",
+    "num_classes = len(np.unique(y_train))\n",
+    "\n",
+    "def build_model(hp):\n",
+    "    model = models.Sequential()\n",
+    "    \n",
+    "    # First layer: Let tuner choose activation function and number of units.\n",
+    "    activation_1 = hp.Choice('activation_1', values=['relu', 'tanh', 'sigmoid'])\n",
+    "    units_1 = hp.Int('units_1', min_value=16, max_value=128, step=16)\n",
+    "    model.add(layers.Dense(units_1, activation=activation_1, input_shape=(num_features,),\n",
+    "                           kernel_regularizer=regularizers.l2(0.01)))\n",
+    "    dropout_rate = hp.Float('dropout_rate', 0.0, 0.5, step=0.1)\n",
+    "    model.add(layers.Dropout(dropout_rate))\n",
+    "    \n",
+    "    # Optional second layer: conditionally add if chosen by the tuner\n",
+    "    if hp.Boolean(\"second_layer\"):\n",
+    "        activation_2 = hp.Choice('activation_2', values=['relu', 'tanh', 'sigmoid'])\n",
+    "        units_2 = hp.Int('units_2', min_value=16, max_value=128, step=16)\n",
+    "        model.add(layers.Dense(units_2, activation=activation_2,\n",
+    "                               kernel_regularizer=regularizers.l2(0.01)))\n",
+    "        # Optional dropout after second layer\n",
+    "        dropout_rate_2 = hp.Float('dropout_rate_2', 0.0, 0.5, step=0.1)\n",
+    "        model.add(layers.Dropout(dropout_rate_2))\n",
+    "    \n",
+    "    # Output layer\n",
+    "    model.add(layers.Dense(num_classes, activation='softmax'))\n",
+    "    \n",
+    "    # Hyperparameter for learning rate\n",
+    "    lr = hp.Choice('learning_rate', values = [1e-4, 1e-3, 1e-2])\n",
+    "    \n",
+    "    model.compile(\n",
+    "        optimizer=tf.keras.optimizers.Adam(learning_rate=lr),\n",
+    "        loss='sparse_categorical_crossentropy',\n",
+    "        metrics=['accuracy']\n",
+    "    )\n",
+    "    return model\n",
+    "\n",
+    "# Initialize the tuner\n",
+    "tuner = kt.RandomSearch(\n",
+    "    build_model,\n",
+    "    objective='val_accuracy',\n",
+    "    max_trials=15,\n",
+    "    executions_per_trial=1,\n",
+    "    overwrite=True,\n",
+    "    directory='my_dir',\n",
+    "    project_name='activation_tuning'\n",
+    ")\n",
+    "\n",
+    "# EarlyStopping callback to stop training when validation loss doesn't improve\n",
+    "early_stop = EarlyStopping(monitor='val_loss', patience=4, restore_best_weights=True)\n",
+    "\n",
+    "# Start hyperparameter search\n",
+    "tuner.search(\n",
+    "    X_train_reduced, y_train,\n",
+    "    validation_data=(X_test_reduced, y_test),\n",
+    "    epochs=EPOCHS,  # Increase epochs for a more robust search if time allows\n",
+    "    callbacks=[early_stop]\n",
+    ")\n",
+    "\n",
+    "# Retrieve best hyperparameters and build the best model\n",
+    "best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]\n",
+    "best_model = tuner.hypermodel.build(best_hps)\n",
+    "print(\"Best Hyperparameters:\")\n",
+    "print(best_hps.values)\n",
+    "\n",
+    "# Train the best model further if needed\n",
+    "history = best_model.fit(\n",
+    "    X_train_reduced, y_train,\n",
+    "    validation_data=(X_test_reduced, y_test),\n",
+    "    epochs=EPOCHS,  # Use best trial's epochs or default\n",
+    "    callbacks=[early_stop],\n",
+    "    #optimzer=tf.keras.optimizers.Adam(learning_rate=0.001)\n",
+    ")\n",
+    "\n",
+    "# Evaluate the best model on the test set\n",
+    "test_loss, test_acc = best_model.evaluate(X_test_reduced, y_test)\n",
+    "print(f\"Test accuracy: {test_acc:.4f}\")"
+   ]
+  }
+ ],
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+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
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+    "name": "ipython",
+    "version": 3
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