6006CW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         1599 non-null   float64\n",
      " 1   volatile acidity      1599 non-null   float64\n",
      " 2   citric acid           1599 non-null   float64\n",
      " 3   residual sugar        1599 non-null   float64\n",
      " 4   chlorides             1599 non-null   float64\n",
      " 5   free sulfur dioxide   1599 non-null   float64\n",
      " 6   total sulfur dioxide  1599 non-null   float64\n",
      " 7   density               1599 non-null   float64\n",
      " 8   pH                    1599 non-null   float64\n",
      " 9   sulphates             1599 non-null   float64\n",
      " 10  alcohol               1599 non-null   float64\n",
      " 11  quality               1599 non-null   int64  \n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('Dataset/winequality-red.csv')\n",
    "#delimiter used since thee file uses \";\" to seperate values\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pre-Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed acidity           0\n",
       "volatile acidity        0\n",
       "citric acid             0\n",
       "residual sugar          0\n",
       "chlorides               0\n",
       "free sulfur dioxide     0\n",
       "total sulfur dioxide    0\n",
       "density                 0\n",
       "pH                      0\n",
       "sulphates               0\n",
       "alcohol                 0\n",
       "quality                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pre-processing goes here\n",
    "df.isnull().sum() #checks if there are any missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1599"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['quality'].value_counts().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f8e91099eb0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='quality', data=df, kind='count')\n",
    "#count how many of each quality there is\n",
    "#now visualise the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use a heatmap to see which values correlate and how.\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(df.corr(), cbar=True, square=True, fmt = '.1f', annot = True, annot_kws={'size':8}, cmap = 'Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9zMui1f4/7jzie37ebG37DMop4Nxpy+cAzyygDVW1Bdiy2AXOlGRbVU32vZ8+jGvt41o3jG/t41o3jK72Pk+9vwKsT/LKJCcD1wB3z2hzN3Bdd/f7YmB/Ve3usSZJmrfejiir6lCSdwJ/DawAPlZVTyS5odu+GdgKbAB2AS8C1/dVjyQtVJ+n3lTVVgZhOH3d5mnzBdzUZw3z1PvpfY/GtfZxrRvGt/ZxrRtGVHsGWSVJmo2vMEpSg0EJJDklyZeTPJLkiSQfHHVN85FkRZKvJvn8qGuZjyTfSvJYkh1Jto26nvlIsjrJp5N8PcnOJJeMuqaWJK/qvteHpwNJ3jPquuYiyXu7/5uPJ/lEklOWdP+eeg/eOQdOr6qDSVYBDwDv7t4WWvaS/HdgEnhFVV056nrmKsm3gMmqWpxnEZdQktuB+6vqI91THadV1fMjLmvOuleMnwYuqqpvj7qeo0lyNoP/k6+uqpeSfArYWlUfX6oaPKJkcFOpqg52i6u6aSx+giQ5B/ivwEdGXcuJIskrgDcCHwWoqu+PU0h23gL843IPyWlWAqcmWQmcxpDnrftkUHa609cdwF7g3qp6aMQlzdX/Bt4PvDziOhaigL9J8nD39tW4+GlgH/Bn3SWPjyQ5fdRFzdM1wCdGXcRcVNXTwC3Ak8BuBs9b/81S1mBQdqrqh1X1OgZvB12Y5LUjLqkpyZXA3qp6eNS1LNClVXUBg1GkbkryxlEXNEcrgQuA26rqfOC7wBHDCC5X3aWCXwb+/6hrmYskP85gAJ1XAv8ROD3J25eyBoNyhu4U6j7g8tFWMieXAr/cXev7JPDmJH8+2pLmrqqe6T73Ap9jMOLUOJgCpqaddXyaQXCOiyuA7VX1z6MuZI7eCvxTVe2rqh8AnwX+y1IWYFACSSaSrO7mT2XwF/P1kRY1B1X1u1V1TlWtY3Aq9XdVtaQ/aRcqyelJzjg8D/wi8Phoq5qbqtoDPJXkVd2qtwBfG2FJ83UtY3La3XkSuDjJad2N17cAO5eygF7fzBkjZwG3d3cCTwI+VVVj9ajNGPop4HODf/esBP5vVf3VaEual3cBd3ansd9kTF6/TXIa8DbgN0Zdy1xV1UNJPg1sBw4BX2WJ39Dx8SBJavDUW5IaDEpJajAoJanBoJSkBoNSkhoMSh33kqxL8ng3P5nk1m7+siRL+uCyxpPPUeqEUlXbgMNDul0GHAT+YWQFaSx4RKllLcnvJ/lGkr/txiH8rST3JZnstq/pXuE8fOR4f5Lt3XTE0WJ3FPn5JOuAG4D3dmMzviHJP3XD7JHkFd14mauW7k+r5cojSi1bSV7P4NXM8xn8W90OHG0AkL3A26rqX5KsZ/Ca3tBfbVpV30qyGThYVbd0+7uPwZB1d3X7/Uz3brFOcB5Rajl7A/C5qnqxqg5w5K87nmkV8H+SPMZgZJxXz3N/H+HfX0W8HvizeX69jlMeUWq5G/aO7SH+/Yf89F8J8F7gn4Gf67b/y7x2VPWl7vT9TcCKqhqLQTrUP48otZx9EfiVJKd2Iw39Urf+W8Dru/mrp7X/D8DuqnoZeAeD3yd/NC8AZ8xYdweDU3aPJvVvDEotW1W1Hfh/wA7gM8D93aZbgBuT/AOwZtqX/Cnw60keBP4zgwF1j+YvGATxjiRv6NbdCfw44zUMmXrm6EEaG0n+J9NuvvS0j6uBq6rqHX3tQ+PHa5RSJ8kfMxj9e8Ooa9Hy4hGlJDV4jVKSGgxKSWowKCWpwaCUpAaDUpIaDEpJavhXoKQyS+ewN/8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a bar plot for each comlumn compared to quality\n",
    "for label in df.columns[:-1]:\n",
    "    plot = plt.figure(figsize=(5,5))\n",
    "    sns.barplot(x='quality', y = label, data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.075</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.9</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.069</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.9964</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.3</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.065</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.9946</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.47</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.073</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.36</td>\n",
       "      <td>0.57</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.5</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.36</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.071</td>\n",
       "      <td>17.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.80</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.7</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.097</td>\n",
       "      <td>15.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.9959</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.54</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.5</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.36</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.071</td>\n",
       "      <td>17.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.80</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.6</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.089</td>\n",
       "      <td>16.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.9943</td>\n",
       "      <td>3.58</td>\n",
       "      <td>0.52</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.114</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.9974</td>\n",
       "      <td>3.26</td>\n",
       "      <td>1.56</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.9</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.18</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.176</td>\n",
       "      <td>52.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.9986</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.88</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0             7.4             0.700         0.00             1.9      0.076   \n",
       "1             7.8             0.880         0.00             2.6      0.098   \n",
       "2             7.8             0.760         0.04             2.3      0.092   \n",
       "3            11.2             0.280         0.56             1.9      0.075   \n",
       "4             7.4             0.700         0.00             1.9      0.076   \n",
       "5             7.4             0.660         0.00             1.8      0.075   \n",
       "6             7.9             0.600         0.06             1.6      0.069   \n",
       "7             7.3             0.650         0.00             1.2      0.065   \n",
       "8             7.8             0.580         0.02             2.0      0.073   \n",
       "9             7.5             0.500         0.36             6.1      0.071   \n",
       "10            6.7             0.580         0.08             1.8      0.097   \n",
       "11            7.5             0.500         0.36             6.1      0.071   \n",
       "12            5.6             0.615         0.00             1.6      0.089   \n",
       "13            7.8             0.610         0.29             1.6      0.114   \n",
       "14            8.9             0.620         0.18             3.8      0.176   \n",
       "\n",
       "    free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                  11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                  25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                  15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                  17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                  11.0                  34.0   0.9978  3.51       0.56   \n",
       "5                  13.0                  40.0   0.9978  3.51       0.56   \n",
       "6                  15.0                  59.0   0.9964  3.30       0.46   \n",
       "7                  15.0                  21.0   0.9946  3.39       0.47   \n",
       "8                   9.0                  18.0   0.9968  3.36       0.57   \n",
       "9                  17.0                 102.0   0.9978  3.35       0.80   \n",
       "10                 15.0                  65.0   0.9959  3.28       0.54   \n",
       "11                 17.0                 102.0   0.9978  3.35       0.80   \n",
       "12                 16.0                  59.0   0.9943  3.58       0.52   \n",
       "13                  9.0                  29.0   0.9974  3.26       1.56   \n",
       "14                 52.0                 145.0   0.9986  3.16       0.88   \n",
       "\n",
       "    alcohol quality  \n",
       "0       9.4       1  \n",
       "1       9.8       1  \n",
       "2       9.8       1  \n",
       "3       9.8       1  \n",
       "4       9.4       1  \n",
       "5       9.4       1  \n",
       "6       9.4       1  \n",
       "7      10.0       1  \n",
       "8       9.5       1  \n",
       "9      10.5       1  \n",
       "10      9.2       1  \n",
       "11     10.5       1  \n",
       "12      9.9       1  \n",
       "13      9.1       1  \n",
       "14      9.2       1  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at the data it seems they the lowest in terms of quality is 3 and hiest is 8 so we make bins(replace data to help representation)\n",
    "# im gonna replace 3-4 to be low (0) 5-7 to be medium(1) and 8-9 good(2) its hard since theyre an odd amount of numbers and theyre all whole numbers so no real \n",
    "bins = [0, 4.5, 7.5, 10] \n",
    "labels = [0, 1, 2]\n",
    "#cut() lets us do this\n",
    "df['quality'] = pd.cut(df['quality'], bins=bins, labels=labels)\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.52835961,  0.96187667, -1.39147228, ...,  1.28864292,\n",
       "        -0.57920652, -0.96024611],\n",
       "       [-0.29854743,  1.96744245, -1.39147228, ..., -0.7199333 ,\n",
       "         0.1289504 , -0.58477711],\n",
       "       [-0.29854743,  1.29706527, -1.18607043, ..., -0.33117661,\n",
       "        -0.04808883, -0.58477711],\n",
       "       ...,\n",
       "       [-1.1603431 , -0.09955388, -0.72391627, ...,  0.70550789,\n",
       "         0.54204194,  0.54162988],\n",
       "       [-1.39015528,  0.65462046, -0.77526673, ...,  1.6773996 ,\n",
       "         0.30598963, -0.20930812],\n",
       "       [-1.33270223, -1.21684919,  1.02199944, ...,  0.51112954,\n",
       "         0.01092425,  0.54162988]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= df.iloc[:, :-1].values #all rows except last column\n",
    "y= df.iloc[:,-1].values #all rows and last column\n",
    "#split the set into two\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "#scale the data down to an amount that makes sense\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting into training/test\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=69)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ALGORTIHMS!!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest not tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "#randomForestClassifier not tuned\n",
    "rfModel = RandomForestClassifier()\n",
    "rfModel.fit(X_train, y_train)\n",
    "rfPred = rfModel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         low       0.00      0.00      0.00        14\n",
      "      medium       0.94      1.00      0.97       301\n",
      "        good       1.00      0.20      0.33         5\n",
      "\n",
      "    accuracy                           0.94       320\n",
      "   macro avg       0.65      0.40      0.43       320\n",
      "weighted avg       0.90      0.94      0.92       320\n",
      "\n",
      "[[  0  14   0]\n",
      " [  1 300   0]\n",
      " [  0   4   1]]\n",
      "0.9491758578431373\n",
      "0.940625\n"
     ]
    }
   ],
   "source": [
    "#Evaluating Random Forest\n",
    "target_names = ['low','medium','good'] \n",
    "print(classification_report(y_test, rfPred, target_names=target_names))\n",
    "print(confusion_matrix(y_test, rfPred))\n",
    "cross_val = cross_val_score(estimator=rfModel, X=X_train, y=y_train, cv=5)\n",
    "print(cross_val.mean())\n",
    "print(accuracy_score(y_test, rfPred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest Tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
      "[CV 1/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=38;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=38;, score=0.957 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=38;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=38;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=38;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=29;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=29;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=29;, score=0.941 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=29;, score=0.945 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=29;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=62;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=62;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=62;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=62;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=62;, score=0.937 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=38;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=38;, score=0.957 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=38;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=38;, score=0.945 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=38;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=79;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=79;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=79;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=79;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=79;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.949 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.949 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.945 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.945 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=72, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=72, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=72, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=72, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=72, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=67;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=67;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=67;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=67;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=67;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=95;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=95;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=95;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=95;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=95;, score=0.937 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=71;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=71;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=95;, score=0.953 total time=   0.2s\n",
      "[CV 2/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=95;, score=0.953 total time=   0.2s\n",
      "[CV 3/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=95;, score=0.941 total time=   0.2s\n",
      "[CV 4/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=95;, score=0.949 total time=   0.2s\n",
      "[CV 5/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=95;, score=0.945 total time=   0.2s\n",
      "[CV 1/5] END bootstrap=True, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=79;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=79;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=79;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=79;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=79;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=25, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=46;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=25, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=46;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=25, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=46;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=25, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=46;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=25, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=46;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=100;, score=0.953 total time=   0.2s\n",
      "[CV 2/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=100;, score=0.949 total time=   0.2s\n",
      "[CV 3/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=100;, score=0.945 total time=   0.2s\n",
      "[CV 4/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=100;, score=0.949 total time=   0.2s\n",
      "[CV 5/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=100;, score=0.941 total time=   0.2s\n",
      "[CV 1/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.949 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.938 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50;, score=0.937 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=38;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=38;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=38;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=38;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=38;, score=0.937 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=41, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=17;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=41, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=17;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=41, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=17;, score=0.945 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=41, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=17;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=41, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=17;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=67;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=67;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=67;, score=0.938 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=67;, score=0.945 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=67;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=5;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=5;, score=0.961 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=5;, score=0.953 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=5;, score=0.938 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=5;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=103, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=29;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=103, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=29;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=103, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=29;, score=0.941 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=103, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=29;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=103, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=29;, score=0.937 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=25;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=25;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=25;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=25;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=25;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=83;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=83;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=83;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=83;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=83;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=46;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=46;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=46;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=46;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=46;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=91;, score=0.953 total time=   0.2s\n",
      "[CV 2/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=91;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=91;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=91;, score=0.949 total time=   0.2s\n",
      "[CV 5/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=91;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=41, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=71;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=41, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=41, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=41, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=41, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=71;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=150, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=1;, score=0.914 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=150, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=1;, score=0.930 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=150, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=1;, score=0.918 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=150, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=1;, score=0.906 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=150, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=1;, score=0.925 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=62;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=62;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=62;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=62;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=62;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=95;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=95;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=95;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=95;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=95;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1;, score=0.918 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1;, score=0.914 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1;, score=0.895 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1;, score=0.902 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1;, score=0.922 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50;, score=0.945 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=87;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=87;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=87;, score=0.941 total time=   0.2s\n",
      "[CV 4/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=87;, score=0.949 total time=   0.2s\n",
      "[CV 5/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=87;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=150, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=100;, score=0.953 total time=   0.2s\n",
      "[CV 2/5] END bootstrap=False, max_depth=150, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=100;, score=0.949 total time=   0.2s\n",
      "[CV 3/5] END bootstrap=False, max_depth=150, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=100;, score=0.949 total time=   0.2s\n",
      "[CV 4/5] END bootstrap=False, max_depth=150, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=100;, score=0.949 total time=   0.2s\n",
      "[CV 5/5] END bootstrap=False, max_depth=150, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=100;, score=0.941 total time=   0.2s\n",
      "[CV 1/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=54;, score=0.957 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=54;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=54;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=54;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=71;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=71;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=71;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=75;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=75;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=75;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=75;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=75;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=10, n_estimators=1;, score=0.930 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=10, n_estimators=1;, score=0.914 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=10, n_estimators=1;, score=0.941 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=10, n_estimators=1;, score=0.902 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=10, n_estimators=1;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=79;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=79;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=79;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=79;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=79;, score=0.937 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=41, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=41, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=41, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=41, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=41, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=87;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=67;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=67;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=67;, score=0.941 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=67;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=67;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=103, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=103, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=103, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=103, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=103, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=67;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=67;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=67;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=67;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=67;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=54;, score=0.938 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=54;, score=0.941 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=54;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=95;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=95;, score=0.953 total time=   0.2s\n",
      "[CV 3/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=95;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=95;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=6, n_estimators=95;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=41, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=83;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=41, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=83;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=41, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=83;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=41, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=83;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=41, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=83;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=41, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=41, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=41, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=41, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=41, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=54;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=54;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=54;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=41, max_features=sqrt, min_samples_leaf=3, min_samples_split=10, n_estimators=58;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=41, max_features=sqrt, min_samples_leaf=3, min_samples_split=10, n_estimators=58;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=41, max_features=sqrt, min_samples_leaf=3, min_samples_split=10, n_estimators=58;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=41, max_features=sqrt, min_samples_leaf=3, min_samples_split=10, n_estimators=58;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=41, max_features=sqrt, min_samples_leaf=3, min_samples_split=10, n_estimators=58;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=72, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=79;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=72, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=79;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=72, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=79;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=72, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=79;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=72, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=79;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=41, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=41, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=41, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=41, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=41, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=71;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=87;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=87;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=87;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=87;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=87;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=38;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=38;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=38;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=38;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=38;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=5;, score=0.957 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=5;, score=0.945 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=5;, score=0.945 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=5;, score=0.945 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=5;, score=0.937 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1;, score=0.922 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1;, score=0.898 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1;, score=0.922 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1;, score=0.934 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=1;, score=0.910 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=54;, score=0.941 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=54;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=54;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=67;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=67;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=67;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=67;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=67;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25;, score=0.957 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25;, score=0.945 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25;, score=0.938 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25;, score=0.945 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=25;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=75;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=75;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=75;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=75;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=75;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=17;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=17;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=17;, score=0.945 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=17;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=25, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=17;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=83;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=83;, score=0.957 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=83;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=83;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=83;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=42;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=42;, score=0.957 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=42;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=42;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=42;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=71;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=71;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=71;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=71;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=71;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=9;, score=0.949 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=9;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=9;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=9;, score=0.941 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=9;, score=0.933 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=54;, score=0.941 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=54;, score=0.945 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=54;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=38;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=38;, score=0.957 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=38;, score=0.938 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=38;, score=0.945 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=38;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=34;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=34;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=34;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=34;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=56, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=34;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=87;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=87;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=87;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=87;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=87;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=118, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=100;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=13;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=13;, score=0.957 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=13;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=13;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=13;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.945 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=5;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=46;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=46;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=46;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=46;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=46;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.945 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=42;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=42;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=42;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=42;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=118, max_features=sqrt, min_samples_leaf=3, min_samples_split=2, n_estimators=42;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=5;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=5;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=5;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=5;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=5;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=25;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=25;, score=0.957 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=25;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=25;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=25;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=79;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=79;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=79;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=79;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=103, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=79;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=42;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=42;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=42;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=42;, score=0.945 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=42;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5;, score=0.957 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=5;, score=0.953 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=17;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=17;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=17;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=17;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=17;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=1;, score=0.926 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=1;, score=0.938 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=1;, score=0.945 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=1;, score=0.922 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=1;, score=0.914 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.949 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.945 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=25;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=9;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=9;, score=0.961 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=9;, score=0.941 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=9;, score=0.953 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=72, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=9;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=54;, score=0.957 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=54;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=54;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=54;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5;, score=0.961 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5;, score=0.949 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5;, score=0.945 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=87, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=5;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=91;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=91;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=91;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=91;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=56, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=91;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=72, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=9;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=72, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=9;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=72, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=9;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=72, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=9;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=72, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=9;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=54;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=54;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=54;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=150, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=54;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=67;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=67;, score=0.957 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=67;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=67;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=103, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=67;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=134, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=46;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=134, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=46;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=134, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=46;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=134, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=46;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=134, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=46;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=95;, score=0.953 total time=   0.2s\n",
      "[CV 2/5] END bootstrap=False, max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=95;, score=0.957 total time=   0.2s\n",
      "[CV 3/5] END bootstrap=False, max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=95;, score=0.945 total time=   0.2s\n",
      "[CV 4/5] END bootstrap=False, max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=95;, score=0.949 total time=   0.2s\n",
      "[CV 5/5] END bootstrap=False, max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=6, n_estimators=95;, score=0.945 total time=   0.2s\n",
      "[CV 1/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.957 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.945 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=13;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=13;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=13;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=13;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=25, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=13;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=True, max_depth=72, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=72, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=72, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=72, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=72, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50;, score=0.945 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=13;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=13;, score=0.957 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=13;, score=0.941 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=13;, score=0.945 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=13;, score=0.929 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.953 total time=   0.2s\n",
      "[CV 2/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.953 total time=   0.2s\n",
      "[CV 3/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.949 total time=   0.2s\n",
      "[CV 4/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.949 total time=   0.2s\n",
      "[CV 5/5] END bootstrap=False, max_depth=134, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.945 total time=   0.2s\n",
      "[CV 1/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=62;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=62;, score=0.949 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=62;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=62;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=False, max_depth=56, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=62;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END bootstrap=True, max_depth=150, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50;, score=0.941 total time=   0.1s\n",
      "[CV 1/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=29;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=29;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=29;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=29;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=True, max_depth=87, max_features=sqrt, min_samples_leaf=3, min_samples_split=6, n_estimators=29;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=25;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=25;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=25;, score=0.941 total time=   0.0s\n",
      "[CV 4/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=25;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END bootstrap=False, max_depth=118, max_features=auto, min_samples_leaf=3, min_samples_split=6, n_estimators=25;, score=0.941 total time=   0.0s\n",
      "Best Parameters:  {'n_estimators': 5, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 150, 'bootstrap': True}\n"
     ]
    }
   ],
   "source": [
    "#importing\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "from sklearn.model_selection import cross_val_score, RandomizedSearchCV\n",
    "\n",
    "# number of trees in the random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 1, stop = 100, num = 25)]\n",
    "# number of features in consideration at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# maximum number of levels allowed in each decision tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 150, num = 10)] \n",
    "# minimum sample number to split a node\n",
    "min_samples_split = [2, 6, 10]\n",
    "# minimum sample number that can be stored in a leaf node \n",
    "min_samples_leaf = [1, 3, 4]\n",
    "#\n",
    "bootstrap = [True, False]\n",
    "\n",
    "#hyper-parameters\n",
    "random_grid = {\n",
    "    'n_estimators': n_estimators,\n",
    "    'max_features': max_features,\n",
    "    'max_depth': max_depth,\n",
    "    'min_samples_split': min_samples_split,\n",
    "    'min_samples_leaf': min_samples_leaf,\n",
    "    'bootstrap': bootstrap\n",
    "    }\n",
    "\n",
    "#randomForestClassifier tuned\n",
    "rfModelTuned = RandomizedSearchCV(rfModel, param_distributions=random_grid, n_iter=100, cv=5, verbose=5, random_state=69)\n",
    "rfModelTuned.fit(X_train, y_train)\n",
    "rfPredTuned = rfModelTuned.predict(X_test)\n",
    "print ('Best Parameters: ', rfModelTuned.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just testing out if the random search worked\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomSearchForest = RandomForestClassifier(n_estimators=5, min_samples_split=6, min_samples_leaf=3, max_features='sqrt', max_depth=103, bootstrap=True)\n",
    "randomSearchForest.fit(X_train, y_train)\n",
    "rfRandomSearch = randomSearchForest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         low       0.00      0.00      0.00        14\n",
      "      medium       0.94      1.00      0.97       301\n",
      "        good       0.00      0.00      0.00         5\n",
      "\n",
      "    accuracy                           0.94       320\n",
      "   macro avg       0.31      0.33      0.32       320\n",
      "weighted avg       0.88      0.94      0.91       320\n",
      "\n",
      "[[  0  14   0]\n",
      " [  1 300   0]\n",
      " [  0   5   0]]\n",
      "0.9460416666666667\n",
      "0.9375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "#Evaluating Random Forest\n",
    "print(classification_report(y_test, rfRandomSearch, target_names=target_names))\n",
    "print(confusion_matrix(y_test, rfRandomSearch))\n",
    "cross_val = cross_val_score(estimator=randomSearchForest, X=X_train, y=y_train, cv=5)\n",
    "print(cross_val.mean())\n",
    "print(accuracy_score(y_test, rfRandomSearch))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support Vetor Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing SVC\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "from sklearn.model_selection import cross_val_score, RandomizedSearchCV\n",
    "\n",
    "svcModel = SVC()\n",
    "svcModel.fit(X_train, y_train)\n",
    "svcPred = svcModel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         low       0.00      0.00      0.00        14\n",
      "      medium       0.94      1.00      0.97       301\n",
      "        good       0.00      0.00      0.00         5\n",
      "\n",
      "    accuracy                           0.94       320\n",
      "   macro avg       0.31      0.33      0.32       320\n",
      "weighted avg       0.88      0.94      0.91       320\n",
      "\n",
      "[[  0  14   0]\n",
      " [  0 301   0]\n",
      " [  0   5   0]]\n",
      "0.9499571078431372\n",
      "0.940625\n"
     ]
    }
   ],
   "source": [
    "#Evaluating SVC\n",
    "print(classification_report(y_test, svcPred, target_names=target_names))\n",
    "print(confusion_matrix(y_test, svcPred))\n",
    "cross_val = cross_val_score(estimator= svcModel, X=X_train, y=y_train, cv=5)\n",
    "print(cross_val.mean())\n",
    "print(accuracy_score(y_test, svcPred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support Vector Classifier Tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n",
      "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.949 total time=   0.1s\n",
      "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.953 total time=   0.1s\n",
      "[CV 1/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.871 total time=   0.0s\n",
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      "[CV 1/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END ....C=0.1, gamma=0.01, kernel=poly;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END .C=0.1, gamma=0.01, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
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      "[CV 5/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.953 total time=   0.1s\n",
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      "[CV 1/5] END .........C=1, gamma=1, kernel=poly;, score=0.836 total time=   0.1s\n",
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      "[CV 1/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.941 total time=   0.0s\n",
      "[CV 2/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.926 total time=   0.0s\n",
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      "[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END .......C=1, gamma=0.1, kernel=poly;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END ....C=1, gamma=0.1, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END ...C=1, gamma=0.01, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END ...C=1, gamma=0.01, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
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      "[CV 5/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.957 total time=   0.1s\n",
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      "[CV 1/5] END ........C=10, gamma=1, kernel=poly;, score=0.816 total time=   0.1s\n",
      "[CV 2/5] END ........C=10, gamma=1, kernel=poly;, score=0.828 total time=   0.1s\n",
      "[CV 3/5] END ........C=10, gamma=1, kernel=poly;, score=0.914 total time=   0.0s\n",
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      "[CV 1/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.914 total time=   0.0s\n",
      "[CV 2/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.906 total time=   0.0s\n",
      "[CV 3/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.914 total time=   0.0s\n",
      "[CV 4/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.898 total time=   0.0s\n",
      "[CV 5/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.906 total time=   0.0s\n",
      "[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.949 total time=   0.0s\n",
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      "[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.938 total time=   0.0s\n",
      "[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.941 total time=   0.0s\n",
      "[CV 1/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.938 total time=   0.0s\n",
      "[CV 2/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.945 total time=   0.0s\n",
      "[CV 3/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.941 total time=   0.0s\n",
      "[CV 4/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.938 total time=   0.0s\n",
      "[CV 5/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.937 total time=   0.0s\n",
      "[CV 1/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.934 total time=   0.0s\n",
      "[CV 2/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.938 total time=   0.0s\n",
      "[CV 3/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 4/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.906 total time=   0.0s\n",
      "[CV 5/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.902 total time=   0.0s\n",
      "[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END ..C=10, gamma=0.01, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END ..C=10, gamma=0.01, kernel=sigmoid;, score=0.949 total time=   0.0s\n",
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      "[CV 5/5] END ..C=10, gamma=0.01, kernel=sigmoid;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END ....C=10, gamma=0.001, kernel=poly;, score=0.953 total time=   0.0s\n",
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      "[CV 1/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.941 total time=   0.1s\n",
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      "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.949 total time=   0.1s\n",
      "[CV 1/5] END .......C=100, gamma=1, kernel=poly;, score=0.816 total time=   0.1s\n",
      "[CV 2/5] END .......C=100, gamma=1, kernel=poly;, score=0.828 total time=   0.1s\n",
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      "[CV 1/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.910 total time=   0.0s\n",
      "[CV 2/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.906 total time=   0.0s\n",
      "[CV 3/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.914 total time=   0.0s\n",
      "[CV 4/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.898 total time=   0.0s\n",
      "[CV 5/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.902 total time=   0.0s\n",
      "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.926 total time=   0.0s\n",
      "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.938 total time=   0.0s\n",
      "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.938 total time=   0.0s\n",
      "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.887 total time=   0.0s\n",
      "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.914 total time=   0.0s\n",
      "[CV 1/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.871 total time=   0.0s\n",
      "[CV 2/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.891 total time=   0.0s\n",
      "[CV 3/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.918 total time=   0.0s\n",
      "[CV 4/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.887 total time=   0.0s\n",
      "[CV 5/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.882 total time=   0.0s\n",
      "[CV 1/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.922 total time=   0.0s\n",
      "[CV 2/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.941 total time=   0.0s\n",
      "[CV 3/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.941 total time=   0.0s\n",
      "[CV 4/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.891 total time=   0.0s\n",
      "[CV 5/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.898 total time=   0.0s\n",
      "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.945 total time=   0.0s\n",
      "[CV 1/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END ....C=100, gamma=0.01, kernel=poly;, score=0.953 total time=   0.0s\n",
      "[CV 1/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=0.949 total time=   0.0s\n",
      "[CV 2/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 4/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=0.938 total time=   0.0s\n",
      "[CV 5/5] END .C=100, gamma=0.01, kernel=sigmoid;, score=0.933 total time=   0.0s\n",
      "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.953 total time=   0.1s\n",
      "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.953 total time=   0.1s\n",
      "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.949 total time=   0.1s\n",
      "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.953 total time=   0.0s\n",
      "[CV 1/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.953 total time=   0.0s\n",
      "[CV 1/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 2/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "[CV 3/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.949 total time=   0.0s\n",
      "[CV 4/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.949 total time=   0.0s\n",
      "[CV 5/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.953 total time=   0.0s\n",
      "Best Parameters:  {'C': 0.1, 'gamma': 1, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'C': [0.1,1, 10, 100],\n",
    "    'gamma': [1,0.1,0.01,0.001],\n",
    "    'kernel': ['rbf', 'poly', 'sigmoid']\n",
    "    }\n",
    "\n",
    "#randomForestClassifier tuned\n",
    "svcModelTuned = GridSearchCV(svcModel, param_grid,refit=True, cv=5, verbose=5)\n",
    "svcModelTuned.fit(X_train, y_train)\n",
    "rsvcPredTuned = svcModelTuned.predict(X_test)\n",
    "print ('Best Parameters: ', svcModelTuned.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridSearchSVC = SVC(C=0.1, gamma= 1, kernel= 'rbf')\n",
    "gridSearchSVC.fit(X_train, y_train)\n",
    "gridSearchSVCPred = gridSearchSVC.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are 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, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         low       0.00      0.00      0.00        14\n",
      "      medium       0.94      1.00      0.97       301\n",
      "        good       0.00      0.00      0.00         5\n",
      "\n",
      "    accuracy                           0.94       320\n",
      "   macro avg       0.31      0.33      0.32       320\n",
      "weighted avg       0.88      0.94      0.91       320\n",
      "\n",
      "[[  0  14   0]\n",
      " [  0 301   0]\n",
      " [  0   5   0]]\n",
      "0.9515257352941177\n",
      "0.940625\n"
     ]
    }
   ],
   "source": [
    "#Evaluating SVC Tuned\n",
    "print(classification_report(y_test, gridSearchSVCPred, target_names=target_names))\n",
    "print(confusion_matrix(y_test, gridSearchSVCPred))\n",
    "cross_val = cross_val_score(estimator= gridSearchSVC, X=X_train, y=y_train, cv=5)\n",
    "print(cross_val.mean())\n",
    "print(accuracy_score(y_test, gridSearchSVCPred))"
   ]
  }
 ],
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