35-Machine Learning with PyCaret.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00694a17",
   "metadata": {},
   "source": [
    "# Machine Learning with PyCaret"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff8356b",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀 \n",
    "</br>\n",
    "In this notebook,  I'm goint to talk about PyCaret library.\n",
    "In short, the notebooks covers the following topics:\n",
    "- Data preprocessing\n",
    "- Buiding the model\n",
    "- Model selection\n",
    "- Model tuning\n",
    "- Model evaluation\n",
    "- Model finalisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d39179",
   "metadata": {},
   "source": [
    "## Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "369ae5fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ba601c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"heart.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9acf93b9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>AGE_50</th>\n",
       "      <th>MD_50</th>\n",
       "      <th>SBP_50</th>\n",
       "      <th>DBP_50</th>\n",
       "      <th>HT_50</th>\n",
       "      <th>WT_50</th>\n",
       "      <th>CHOL_50</th>\n",
       "      <th>SES</th>\n",
       "      <th>CL_STATUS</th>\n",
       "      <th>MD_62</th>\n",
       "      <th>SBP_62</th>\n",
       "      <th>DBP_62</th>\n",
       "      <th>CHOL_62</th>\n",
       "      <th>WT_62</th>\n",
       "      <th>IHD_DX</th>\n",
       "      <th>DEATH</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>90</td>\n",
       "      <td>70</td>\n",
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       "      <td>80</td>\n",
       "      <td>72</td>\n",
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       "      <td>8</td>\n",
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       "      <td>118</td>\n",
       "      <td>68</td>\n",
       "      <td>209</td>\n",
       "      <td>227</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "      <td>3</td>\n",
       "      <td>118</td>\n",
       "      <td>74</td>\n",
       "      <td>66</td>\n",
       "      <td>134</td>\n",
       "      <td>243</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
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       "      <td>56</td>\n",
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       "   AGE_50  MD_50  SBP_50  DBP_50  HT_50  WT_50  CHOL_50  SES  CL_STATUS  \\\n",
       "0      42      1     110      65     64    147      291    2          8   \n",
       "1      53      1     130      72     69    167      278    1          6   \n",
       "2      53      2     120      90     70    222      342    4          8   \n",
       "3      48      4     120      80     72    229      239    4          8   \n",
       "4      53      3     118      74     66    134      243    3          8   \n",
       "\n",
       "   MD_62  SBP_62  DBP_62  CHOL_62  WT_62  IHD_DX  DEATH  \n",
       "0      4     120      78      271    146       2      1  \n",
       "1      2     122      68      250    165       9      1  \n",
       "2      1     132      90      304    223       2      1  \n",
       "3      2     118      68      209    227       3      1  \n",
       "4      5     118      56      261    138       2      1  "
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6bc4973e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 16)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e28a446",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "765e6562",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.sample(frac = 0.95, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af1ca375",
   "metadata": {},
   "outputs": [
    {
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       "     AGE_50  MD_50  SBP_50  DBP_50  HT_50  WT_50  CHOL_50  SES  CL_STATUS  \\\n",
       "18       47      2     130      80     67    147      334    1          8   \n",
       "170      64      4     104      74     63    146      300    3          8   \n",
       "107      45      3     130     100     72    168      252    5          3   \n",
       "98       49      4     120      80     63    142      283    3          8   \n",
       "177      22      4     120      90     69    192      240    5          8   \n",
       "\n",
       "     MD_62  SBP_62  DBP_62  CHOL_62  WT_62  IHD_DX  DEATH  \n",
       "18       3     138      85      303    147       2      0  \n",
       "170      1     130      80      249    143       0      0  \n",
       "107      1     150     100      234    176       0      0  \n",
       "98       5     116      88      311    160       0      1  \n",
       "177      1     150     105      269    201       0      0  "
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e8a5bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_unseen = df.drop(data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41c2326e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.reset_index(inplace=True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b79bf93",
   "metadata": {},
   "outputs": [
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       "   AGE_50  MD_50  SBP_50  DBP_50  HT_50  WT_50  CHOL_50  SES  CL_STATUS  \\\n",
       "0      47      2     130      80     67    147      334    1          8   \n",
       "1      64      4     104      74     63    146      300    3          8   \n",
       "2      45      3     130     100     72    168      252    5          3   \n",
       "3      49      4     120      80     63    142      283    3          8   \n",
       "4      22      4     120      90     69    192      240    5          8   \n",
       "\n",
       "   MD_62  SBP_62  DBP_62  CHOL_62  WT_62  IHD_DX  DEATH  \n",
       "0      3     138      85      303    147       2      0  \n",
       "1      1     130      80      249    143       0      0  \n",
       "2      1     150     100      234    176       0      0  \n",
       "3      5     116      88      311    160       0      1  \n",
       "4      1     150     105      269    201       0      0  "
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d2a5a85d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_unseen.reset_index(inplace=True, drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "20911eea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    131\n",
       "1     59\n",
       "Name: DEATH, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data[\"DEATH\"].value_counts()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9cddb582",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.classification import * "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5dfe9c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "71d94497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9c32e_row29_col1, #T_9c32e_row44_col1, #T_9c32e_row58_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9c32e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9c32e_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_9c32e_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_9c32e_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "      <td id=\"T_9c32e_row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_9c32e_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_9c32e_row1_col1\" class=\"data row1 col1\" >DEATH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_9c32e_row2_col0\" class=\"data row2 col0\" >Target Type</td>\n",
       "      <td id=\"T_9c32e_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_9c32e_row3_col0\" class=\"data row3 col0\" >Label Encoded</td>\n",
       "      <td id=\"T_9c32e_row3_col1\" class=\"data row3 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_9c32e_row4_col0\" class=\"data row4 col0\" >Original Data</td>\n",
       "      <td id=\"T_9c32e_row4_col1\" class=\"data row4 col1\" >(190, 16)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_9c32e_row5_col0\" class=\"data row5 col0\" >Missing Values</td>\n",
       "      <td id=\"T_9c32e_row5_col1\" class=\"data row5 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_9c32e_row6_col0\" class=\"data row6 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_9c32e_row6_col1\" class=\"data row6 col1\" >9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_9c32e_row7_col0\" class=\"data row7 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_9c32e_row7_col1\" class=\"data row7 col1\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_9c32e_row8_col0\" class=\"data row8 col0\" >Ordinal Features</td>\n",
       "      <td id=\"T_9c32e_row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_9c32e_row9_col0\" class=\"data row9 col0\" >High Cardinality Features</td>\n",
       "      <td id=\"T_9c32e_row9_col1\" class=\"data row9 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_9c32e_row10_col0\" class=\"data row10 col0\" >High Cardinality Method</td>\n",
       "      <td id=\"T_9c32e_row10_col1\" class=\"data row10 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_9c32e_row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_9c32e_row11_col1\" class=\"data row11 col1\" >(152, 53)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_9c32e_row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_9c32e_row12_col1\" class=\"data row12 col1\" >(38, 53)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_9c32e_row13_col0\" class=\"data row13 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_9c32e_row13_col1\" class=\"data row13 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_9c32e_row14_col0\" class=\"data row14 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_9c32e_row14_col1\" class=\"data row14 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_9c32e_row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_9c32e_row15_col1\" class=\"data row15 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_9c32e_row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
       "      <td id=\"T_9c32e_row16_col1\" class=\"data row16 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_9c32e_row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_9c32e_row17_col1\" class=\"data row17 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_9c32e_row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
       "      <td id=\"T_9c32e_row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_9c32e_row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_9c32e_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_9c32e_row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_9c32e_row20_col1\" class=\"data row20 col1\" >clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_9c32e_row21_col0\" class=\"data row21 col0\" >USI</td>\n",
       "      <td id=\"T_9c32e_row21_col1\" class=\"data row21 col1\" >4be9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_9c32e_row22_col0\" class=\"data row22 col0\" >Imputation Type</td>\n",
       "      <td id=\"T_9c32e_row22_col1\" class=\"data row22 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_9c32e_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Iteration</td>\n",
       "      <td id=\"T_9c32e_row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_9c32e_row24_col0\" class=\"data row24 col0\" >Numeric Imputer</td>\n",
       "      <td id=\"T_9c32e_row24_col1\" class=\"data row24 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_9c32e_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Numeric Model</td>\n",
       "      <td id=\"T_9c32e_row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_9c32e_row26_col0\" class=\"data row26 col0\" >Categorical Imputer</td>\n",
       "      <td id=\"T_9c32e_row26_col1\" class=\"data row26 col1\" >constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_9c32e_row27_col0\" class=\"data row27 col0\" >Iterative Imputation Categorical Model</td>\n",
       "      <td id=\"T_9c32e_row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_9c32e_row28_col0\" class=\"data row28 col0\" >Unknown Categoricals Handling</td>\n",
       "      <td id=\"T_9c32e_row28_col1\" class=\"data row28 col1\" >least_frequent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_9c32e_row29_col0\" class=\"data row29 col0\" >Normalize</td>\n",
       "      <td id=\"T_9c32e_row29_col1\" class=\"data row29 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_9c32e_row30_col0\" class=\"data row30 col0\" >Normalize Method</td>\n",
       "      <td id=\"T_9c32e_row30_col1\" class=\"data row30 col1\" >minmax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_9c32e_row31_col0\" class=\"data row31 col0\" >Transformation</td>\n",
       "      <td id=\"T_9c32e_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_9c32e_row32_col0\" class=\"data row32 col0\" >Transformation Method</td>\n",
       "      <td id=\"T_9c32e_row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_9c32e_row33_col0\" class=\"data row33 col0\" >PCA</td>\n",
       "      <td id=\"T_9c32e_row33_col1\" class=\"data row33 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_9c32e_row34_col0\" class=\"data row34 col0\" >PCA Method</td>\n",
       "      <td id=\"T_9c32e_row34_col1\" class=\"data row34 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_9c32e_row35_col0\" class=\"data row35 col0\" >PCA Components</td>\n",
       "      <td id=\"T_9c32e_row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_9c32e_row36_col0\" class=\"data row36 col0\" >Ignore Low Variance</td>\n",
       "      <td id=\"T_9c32e_row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_9c32e_row37_col0\" class=\"data row37 col0\" >Combine Rare Levels</td>\n",
       "      <td id=\"T_9c32e_row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_9c32e_row38_col0\" class=\"data row38 col0\" >Rare Level Threshold</td>\n",
       "      <td id=\"T_9c32e_row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_9c32e_row39_col0\" class=\"data row39 col0\" >Numeric Binning</td>\n",
       "      <td id=\"T_9c32e_row39_col1\" class=\"data row39 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_9c32e_row40_col0\" class=\"data row40 col0\" >Remove Outliers</td>\n",
       "      <td id=\"T_9c32e_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_9c32e_row41_col0\" class=\"data row41 col0\" >Outliers Threshold</td>\n",
       "      <td id=\"T_9c32e_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_9c32e_row42_col0\" class=\"data row42 col0\" >Remove Multicollinearity</td>\n",
       "      <td id=\"T_9c32e_row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_9c32e_row43_col0\" class=\"data row43 col0\" >Multicollinearity Threshold</td>\n",
       "      <td id=\"T_9c32e_row43_col1\" class=\"data row43 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_9c32e_row44_col0\" class=\"data row44 col0\" >Remove Perfect Collinearity</td>\n",
       "      <td id=\"T_9c32e_row44_col1\" class=\"data row44 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_9c32e_row45_col0\" class=\"data row45 col0\" >Clustering</td>\n",
       "      <td id=\"T_9c32e_row45_col1\" class=\"data row45 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_9c32e_row46_col0\" class=\"data row46 col0\" >Clustering Iteration</td>\n",
       "      <td id=\"T_9c32e_row46_col1\" class=\"data row46 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_9c32e_row47_col0\" class=\"data row47 col0\" >Polynomial Features</td>\n",
       "      <td id=\"T_9c32e_row47_col1\" class=\"data row47 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_9c32e_row48_col0\" class=\"data row48 col0\" >Polynomial Degree</td>\n",
       "      <td id=\"T_9c32e_row48_col1\" class=\"data row48 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_9c32e_row49_col0\" class=\"data row49 col0\" >Trignometry Features</td>\n",
       "      <td id=\"T_9c32e_row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_9c32e_row50_col0\" class=\"data row50 col0\" >Polynomial Threshold</td>\n",
       "      <td id=\"T_9c32e_row50_col1\" class=\"data row50 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_9c32e_row51_col0\" class=\"data row51 col0\" >Group Features</td>\n",
       "      <td id=\"T_9c32e_row51_col1\" class=\"data row51 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_9c32e_row52_col0\" class=\"data row52 col0\" >Feature Selection</td>\n",
       "      <td id=\"T_9c32e_row52_col1\" class=\"data row52 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_9c32e_row53_col0\" class=\"data row53 col0\" >Feature Selection Method</td>\n",
       "      <td id=\"T_9c32e_row53_col1\" class=\"data row53 col1\" >classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_9c32e_row54_col0\" class=\"data row54 col0\" >Features Selection Threshold</td>\n",
       "      <td id=\"T_9c32e_row54_col1\" class=\"data row54 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_9c32e_row55_col0\" class=\"data row55 col0\" >Feature Interaction</td>\n",
       "      <td id=\"T_9c32e_row55_col1\" class=\"data row55 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "      <td id=\"T_9c32e_row56_col0\" class=\"data row56 col0\" >Feature Ratio</td>\n",
       "      <td id=\"T_9c32e_row56_col1\" class=\"data row56 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "      <td id=\"T_9c32e_row57_col0\" class=\"data row57 col0\" >Interaction Threshold</td>\n",
       "      <td id=\"T_9c32e_row57_col1\" class=\"data row57 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
       "      <td id=\"T_9c32e_row58_col0\" class=\"data row58 col0\" >Fix Imbalance</td>\n",
       "      <td id=\"T_9c32e_row58_col1\" class=\"data row58 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9c32e_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
       "      <td id=\"T_9c32e_row59_col0\" class=\"data row59 col0\" >Fix Imbalance Method</td>\n",
       "      <td id=\"T_9c32e_row59_col1\" class=\"data row59 col1\" >RandomOverSampler</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x27a0483a910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = setup(data=data,\n",
    "             target = \"DEATH\",\n",
    "             normalize = True,\n",
    "             normalize_method=\"minmax\",\n",
    "             train_size = 0.8,\n",
    "             fix_imbalance= True,\n",
    "             fix_imbalance_method=RandomOverSampler(),\n",
    "             session_id = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b061971f",
   "metadata": {},
   "source": [
    "## Building the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "914af03a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_8ece1_row10_col0, #T_8ece1_row10_col1, #T_8ece1_row10_col2, #T_8ece1_row10_col3, #T_8ece1_row10_col4, #T_8ece1_row10_col5, #T_8ece1_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_8ece1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8ece1_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_8ece1_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_8ece1_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_8ece1_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_8ece1_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_8ece1_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_8ece1_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_8ece1_row0_col0\" class=\"data row0 col0\" >0.5000</td>\n",
       "      <td id=\"T_8ece1_row0_col1\" class=\"data row0 col1\" >0.5636</td>\n",
       "      <td id=\"T_8ece1_row0_col2\" class=\"data row0 col2\" >0.8000</td>\n",
       "      <td id=\"T_8ece1_row0_col3\" class=\"data row0 col3\" >0.3636</td>\n",
       "      <td id=\"T_8ece1_row0_col4\" class=\"data row0 col4\" >0.5000</td>\n",
       "      <td id=\"T_8ece1_row0_col5\" class=\"data row0 col5\" >0.1233</td>\n",
       "      <td id=\"T_8ece1_row0_col6\" class=\"data row0 col6\" >0.1636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_8ece1_row1_col0\" class=\"data row1 col0\" >0.3750</td>\n",
       "      <td id=\"T_8ece1_row1_col1\" class=\"data row1 col1\" >0.4500</td>\n",
       "      <td id=\"T_8ece1_row1_col2\" class=\"data row1 col2\" >0.3333</td>\n",
       "      <td id=\"T_8ece1_row1_col3\" class=\"data row1 col3\" >0.2500</td>\n",
       "      <td id=\"T_8ece1_row1_col4\" class=\"data row1 col4\" >0.2857</td>\n",
       "      <td id=\"T_8ece1_row1_col5\" class=\"data row1 col5\" >-0.2500</td>\n",
       "      <td id=\"T_8ece1_row1_col6\" class=\"data row1 col6\" >-0.2582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_8ece1_row2_col0\" class=\"data row2 col0\" >0.5333</td>\n",
       "      <td id=\"T_8ece1_row2_col1\" class=\"data row2 col1\" >0.4200</td>\n",
       "      <td id=\"T_8ece1_row2_col2\" class=\"data row2 col2\" >0.2000</td>\n",
       "      <td id=\"T_8ece1_row2_col3\" class=\"data row2 col3\" >0.2500</td>\n",
       "      <td id=\"T_8ece1_row2_col4\" class=\"data row2 col4\" >0.2222</td>\n",
       "      <td id=\"T_8ece1_row2_col5\" class=\"data row2 col5\" >-0.1053</td>\n",
       "      <td id=\"T_8ece1_row2_col6\" class=\"data row2 col6\" >-0.1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_8ece1_row3_col0\" class=\"data row3 col0\" >0.5333</td>\n",
       "      <td id=\"T_8ece1_row3_col1\" class=\"data row3 col1\" >0.5400</td>\n",
       "      <td id=\"T_8ece1_row3_col2\" class=\"data row3 col2\" >0.2000</td>\n",
       "      <td id=\"T_8ece1_row3_col3\" class=\"data row3 col3\" >0.2500</td>\n",
       "      <td id=\"T_8ece1_row3_col4\" class=\"data row3 col4\" >0.2222</td>\n",
       "      <td id=\"T_8ece1_row3_col5\" class=\"data row3 col5\" >-0.1053</td>\n",
       "      <td id=\"T_8ece1_row3_col6\" class=\"data row3 col6\" >-0.1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_8ece1_row4_col0\" class=\"data row4 col0\" >0.4667</td>\n",
       "      <td id=\"T_8ece1_row4_col1\" class=\"data row4 col1\" >0.4700</td>\n",
       "      <td id=\"T_8ece1_row4_col2\" class=\"data row4 col2\" >0.4000</td>\n",
       "      <td id=\"T_8ece1_row4_col3\" class=\"data row4 col3\" >0.2857</td>\n",
       "      <td id=\"T_8ece1_row4_col4\" class=\"data row4 col4\" >0.3333</td>\n",
       "      <td id=\"T_8ece1_row4_col5\" class=\"data row4 col5\" >-0.0909</td>\n",
       "      <td id=\"T_8ece1_row4_col6\" class=\"data row4 col6\" >-0.0945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_8ece1_row5_col0\" class=\"data row5 col0\" >0.5333</td>\n",
       "      <td id=\"T_8ece1_row5_col1\" class=\"data row5 col1\" >0.5700</td>\n",
       "      <td id=\"T_8ece1_row5_col2\" class=\"data row5 col2\" >0.2000</td>\n",
       "      <td id=\"T_8ece1_row5_col3\" class=\"data row5 col3\" >0.2500</td>\n",
       "      <td id=\"T_8ece1_row5_col4\" class=\"data row5 col4\" >0.2222</td>\n",
       "      <td id=\"T_8ece1_row5_col5\" class=\"data row5 col5\" >-0.1053</td>\n",
       "      <td id=\"T_8ece1_row5_col6\" class=\"data row5 col6\" >-0.1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_8ece1_row6_col0\" class=\"data row6 col0\" >0.6000</td>\n",
       "      <td id=\"T_8ece1_row6_col1\" class=\"data row6 col1\" >0.6400</td>\n",
       "      <td id=\"T_8ece1_row6_col2\" class=\"data row6 col2\" >0.8000</td>\n",
       "      <td id=\"T_8ece1_row6_col3\" class=\"data row6 col3\" >0.4444</td>\n",
       "      <td id=\"T_8ece1_row6_col4\" class=\"data row6 col4\" >0.5714</td>\n",
       "      <td id=\"T_8ece1_row6_col5\" class=\"data row6 col5\" >0.2500</td>\n",
       "      <td id=\"T_8ece1_row6_col6\" class=\"data row6 col6\" >0.2887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_8ece1_row7_col0\" class=\"data row7 col0\" >0.4667</td>\n",
       "      <td id=\"T_8ece1_row7_col1\" class=\"data row7 col1\" >0.4500</td>\n",
       "      <td id=\"T_8ece1_row7_col2\" class=\"data row7 col2\" >0.4000</td>\n",
       "      <td id=\"T_8ece1_row7_col3\" class=\"data row7 col3\" >0.2857</td>\n",
       "      <td id=\"T_8ece1_row7_col4\" class=\"data row7 col4\" >0.3333</td>\n",
       "      <td id=\"T_8ece1_row7_col5\" class=\"data row7 col5\" >-0.0909</td>\n",
       "      <td id=\"T_8ece1_row7_col6\" class=\"data row7 col6\" >-0.0945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_8ece1_row8_col0\" class=\"data row8 col0\" >0.8000</td>\n",
       "      <td id=\"T_8ece1_row8_col1\" class=\"data row8 col1\" >0.9000</td>\n",
       "      <td id=\"T_8ece1_row8_col2\" class=\"data row8 col2\" >1.0000</td>\n",
       "      <td id=\"T_8ece1_row8_col3\" class=\"data row8 col3\" >0.6250</td>\n",
       "      <td id=\"T_8ece1_row8_col4\" class=\"data row8 col4\" >0.7692</td>\n",
       "      <td id=\"T_8ece1_row8_col5\" class=\"data row8 col5\" >0.6087</td>\n",
       "      <td id=\"T_8ece1_row8_col6\" class=\"data row8 col6\" >0.6614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_8ece1_row9_col0\" class=\"data row9 col0\" >0.6000</td>\n",
       "      <td id=\"T_8ece1_row9_col1\" class=\"data row9 col1\" >0.7200</td>\n",
       "      <td id=\"T_8ece1_row9_col2\" class=\"data row9 col2\" >0.6000</td>\n",
       "      <td id=\"T_8ece1_row9_col3\" class=\"data row9 col3\" >0.4286</td>\n",
       "      <td id=\"T_8ece1_row9_col4\" class=\"data row9 col4\" >0.5000</td>\n",
       "      <td id=\"T_8ece1_row9_col5\" class=\"data row9 col5\" >0.1818</td>\n",
       "      <td id=\"T_8ece1_row9_col6\" class=\"data row9 col6\" >0.1890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_8ece1_row10_col0\" class=\"data row10 col0\" >0.5408</td>\n",
       "      <td id=\"T_8ece1_row10_col1\" class=\"data row10 col1\" >0.5724</td>\n",
       "      <td id=\"T_8ece1_row10_col2\" class=\"data row10 col2\" >0.4933</td>\n",
       "      <td id=\"T_8ece1_row10_col3\" class=\"data row10 col3\" >0.3433</td>\n",
       "      <td id=\"T_8ece1_row10_col4\" class=\"data row10 col4\" >0.3960</td>\n",
       "      <td id=\"T_8ece1_row10_col5\" class=\"data row10 col5\" >0.0416</td>\n",
       "      <td id=\"T_8ece1_row10_col6\" class=\"data row10 col6\" >0.0536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8ece1_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_8ece1_row11_col0\" class=\"data row11 col0\" >0.1070</td>\n",
       "      <td id=\"T_8ece1_row11_col1\" class=\"data row11 col1\" >0.1409</td>\n",
       "      <td id=\"T_8ece1_row11_col2\" class=\"data row11 col2\" >0.2752</td>\n",
       "      <td id=\"T_8ece1_row11_col3\" class=\"data row11 col3\" >0.1178</td>\n",
       "      <td id=\"T_8ece1_row11_col4\" class=\"data row11 col4\" >0.1739</td>\n",
       "      <td id=\"T_8ece1_row11_col5\" class=\"data row11 col5\" >0.2400</td>\n",
       "      <td id=\"T_8ece1_row11_col6\" class=\"data row11 col6\" >0.2595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x27a047c75b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn = create_model(\"knn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e651c12d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_18d30_row10_col0, #T_18d30_row10_col1, #T_18d30_row10_col2, #T_18d30_row10_col3, #T_18d30_row10_col4, #T_18d30_row10_col5, #T_18d30_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_18d30\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_18d30_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_18d30_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_18d30_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_18d30_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_18d30_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_18d30_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_18d30_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_18d30_row0_col0\" class=\"data row0 col0\" >0.7500</td>\n",
       "      <td id=\"T_18d30_row0_col1\" class=\"data row0 col1\" >0.6545</td>\n",
       "      <td id=\"T_18d30_row0_col2\" class=\"data row0 col2\" >0.8000</td>\n",
       "      <td id=\"T_18d30_row0_col3\" class=\"data row0 col3\" >0.5714</td>\n",
       "      <td id=\"T_18d30_row0_col4\" class=\"data row0 col4\" >0.6667</td>\n",
       "      <td id=\"T_18d30_row0_col5\" class=\"data row0 col5\" >0.4754</td>\n",
       "      <td id=\"T_18d30_row0_col6\" class=\"data row0 col6\" >0.4927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_18d30_row1_col0\" class=\"data row1 col0\" >0.6250</td>\n",
       "      <td id=\"T_18d30_row1_col1\" class=\"data row1 col1\" >0.6167</td>\n",
       "      <td id=\"T_18d30_row1_col2\" class=\"data row1 col2\" >0.5000</td>\n",
       "      <td id=\"T_18d30_row1_col3\" class=\"data row1 col3\" >0.5000</td>\n",
       "      <td id=\"T_18d30_row1_col4\" class=\"data row1 col4\" >0.5000</td>\n",
       "      <td id=\"T_18d30_row1_col5\" class=\"data row1 col5\" >0.2000</td>\n",
       "      <td id=\"T_18d30_row1_col6\" class=\"data row1 col6\" >0.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_18d30_row2_col0\" class=\"data row2 col0\" >0.6667</td>\n",
       "      <td id=\"T_18d30_row2_col1\" class=\"data row2 col1\" >0.8000</td>\n",
       "      <td id=\"T_18d30_row2_col2\" class=\"data row2 col2\" >0.8000</td>\n",
       "      <td id=\"T_18d30_row2_col3\" class=\"data row2 col3\" >0.5000</td>\n",
       "      <td id=\"T_18d30_row2_col4\" class=\"data row2 col4\" >0.6154</td>\n",
       "      <td id=\"T_18d30_row2_col5\" class=\"data row2 col5\" >0.3478</td>\n",
       "      <td id=\"T_18d30_row2_col6\" class=\"data row2 col6\" >0.3780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_18d30_row3_col0\" class=\"data row3 col0\" >0.6000</td>\n",
       "      <td id=\"T_18d30_row3_col1\" class=\"data row3 col1\" >0.6200</td>\n",
       "      <td id=\"T_18d30_row3_col2\" class=\"data row3 col2\" >0.4000</td>\n",
       "      <td id=\"T_18d30_row3_col3\" class=\"data row3 col3\" >0.4000</td>\n",
       "      <td id=\"T_18d30_row3_col4\" class=\"data row3 col4\" >0.4000</td>\n",
       "      <td id=\"T_18d30_row3_col5\" class=\"data row3 col5\" >0.1000</td>\n",
       "      <td id=\"T_18d30_row3_col6\" class=\"data row3 col6\" >0.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_18d30_row4_col0\" class=\"data row4 col0\" >0.6667</td>\n",
       "      <td id=\"T_18d30_row4_col1\" class=\"data row4 col1\" >0.6400</td>\n",
       "      <td id=\"T_18d30_row4_col2\" class=\"data row4 col2\" >0.4000</td>\n",
       "      <td id=\"T_18d30_row4_col3\" class=\"data row4 col3\" >0.5000</td>\n",
       "      <td id=\"T_18d30_row4_col4\" class=\"data row4 col4\" >0.4444</td>\n",
       "      <td id=\"T_18d30_row4_col5\" class=\"data row4 col5\" >0.2105</td>\n",
       "      <td id=\"T_18d30_row4_col6\" class=\"data row4 col6\" >0.2132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_18d30_row5_col0\" class=\"data row5 col0\" >0.6000</td>\n",
       "      <td id=\"T_18d30_row5_col1\" class=\"data row5 col1\" >0.5200</td>\n",
       "      <td id=\"T_18d30_row5_col2\" class=\"data row5 col2\" >0.6000</td>\n",
       "      <td id=\"T_18d30_row5_col3\" class=\"data row5 col3\" >0.4286</td>\n",
       "      <td id=\"T_18d30_row5_col4\" class=\"data row5 col4\" >0.5000</td>\n",
       "      <td id=\"T_18d30_row5_col5\" class=\"data row5 col5\" >0.1818</td>\n",
       "      <td id=\"T_18d30_row5_col6\" class=\"data row5 col6\" >0.1890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_18d30_row6_col0\" class=\"data row6 col0\" >0.5333</td>\n",
       "      <td id=\"T_18d30_row6_col1\" class=\"data row6 col1\" >0.6600</td>\n",
       "      <td id=\"T_18d30_row6_col2\" class=\"data row6 col2\" >0.2000</td>\n",
       "      <td id=\"T_18d30_row6_col3\" class=\"data row6 col3\" >0.2500</td>\n",
       "      <td id=\"T_18d30_row6_col4\" class=\"data row6 col4\" >0.2222</td>\n",
       "      <td id=\"T_18d30_row6_col5\" class=\"data row6 col5\" >-0.1053</td>\n",
       "      <td id=\"T_18d30_row6_col6\" class=\"data row6 col6\" >-0.1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_18d30_row7_col0\" class=\"data row7 col0\" >0.6000</td>\n",
       "      <td id=\"T_18d30_row7_col1\" class=\"data row7 col1\" >0.5600</td>\n",
       "      <td id=\"T_18d30_row7_col2\" class=\"data row7 col2\" >0.4000</td>\n",
       "      <td id=\"T_18d30_row7_col3\" class=\"data row7 col3\" >0.4000</td>\n",
       "      <td id=\"T_18d30_row7_col4\" class=\"data row7 col4\" >0.4000</td>\n",
       "      <td id=\"T_18d30_row7_col5\" class=\"data row7 col5\" >0.1000</td>\n",
       "      <td id=\"T_18d30_row7_col6\" class=\"data row7 col6\" >0.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_18d30_row8_col0\" class=\"data row8 col0\" >0.7333</td>\n",
       "      <td id=\"T_18d30_row8_col1\" class=\"data row8 col1\" >0.7600</td>\n",
       "      <td id=\"T_18d30_row8_col2\" class=\"data row8 col2\" >0.8000</td>\n",
       "      <td id=\"T_18d30_row8_col3\" class=\"data row8 col3\" >0.5714</td>\n",
       "      <td id=\"T_18d30_row8_col4\" class=\"data row8 col4\" >0.6667</td>\n",
       "      <td id=\"T_18d30_row8_col5\" class=\"data row8 col5\" >0.4545</td>\n",
       "      <td id=\"T_18d30_row8_col6\" class=\"data row8 col6\" >0.4725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_18d30_row9_col0\" class=\"data row9 col0\" >0.5333</td>\n",
       "      <td id=\"T_18d30_row9_col1\" class=\"data row9 col1\" >0.5800</td>\n",
       "      <td id=\"T_18d30_row9_col2\" class=\"data row9 col2\" >0.4000</td>\n",
       "      <td id=\"T_18d30_row9_col3\" class=\"data row9 col3\" >0.3333</td>\n",
       "      <td id=\"T_18d30_row9_col4\" class=\"data row9 col4\" >0.3636</td>\n",
       "      <td id=\"T_18d30_row9_col5\" class=\"data row9 col5\" >0.0000</td>\n",
       "      <td id=\"T_18d30_row9_col6\" class=\"data row9 col6\" >0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_18d30_row10_col0\" class=\"data row10 col0\" >0.6308</td>\n",
       "      <td id=\"T_18d30_row10_col1\" class=\"data row10 col1\" >0.6411</td>\n",
       "      <td id=\"T_18d30_row10_col2\" class=\"data row10 col2\" >0.5300</td>\n",
       "      <td id=\"T_18d30_row10_col3\" class=\"data row10 col3\" >0.4455</td>\n",
       "      <td id=\"T_18d30_row10_col4\" class=\"data row10 col4\" >0.4779</td>\n",
       "      <td id=\"T_18d30_row10_col5\" class=\"data row10 col5\" >0.1965</td>\n",
       "      <td id=\"T_18d30_row10_col6\" class=\"data row10 col6\" >0.2039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_18d30_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_18d30_row11_col0\" class=\"data row11 col0\" >0.0701</td>\n",
       "      <td id=\"T_18d30_row11_col1\" class=\"data row11 col1\" >0.0812</td>\n",
       "      <td id=\"T_18d30_row11_col2\" class=\"data row11 col2\" >0.2002</td>\n",
       "      <td id=\"T_18d30_row11_col3\" class=\"data row11 col3\" >0.0979</td>\n",
       "      <td id=\"T_18d30_row11_col4\" class=\"data row11 col4\" >0.1352</td>\n",
       "      <td id=\"T_18d30_row11_col5\" class=\"data row11 col5\" >0.1778</td>\n",
       "      <td id=\"T_18d30_row11_col6\" class=\"data row11 col6\" >0.1859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x27a045c9520>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_knn = tune_model(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e79c4c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_knn, plot = \"auc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5c6b4c8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_knn, plot=\"pr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "78478184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_675b4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_675b4_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_675b4_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_675b4_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_675b4_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_675b4_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_675b4_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_675b4_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_675b4_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_675b4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_675b4_row0_col0\" class=\"data row0 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_675b4_row0_col1\" class=\"data row0 col1\" >0.5263</td>\n",
       "      <td id=\"T_675b4_row0_col2\" class=\"data row0 col2\" >0.6125</td>\n",
       "      <td id=\"T_675b4_row0_col3\" class=\"data row0 col3\" >0.5000</td>\n",
       "      <td id=\"T_675b4_row0_col4\" class=\"data row0 col4\" >0.2222</td>\n",
       "      <td id=\"T_675b4_row0_col5\" class=\"data row0 col5\" >0.3077</td>\n",
       "      <td id=\"T_675b4_row0_col6\" class=\"data row0 col6\" >0.0229</td>\n",
       "      <td id=\"T_675b4_row0_col7\" class=\"data row0 col7\" >0.0272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x27a045bdac0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>AGE_50</th>\n",
       "      <th>SBP_50</th>\n",
       "      <th>DBP_50</th>\n",
       "      <th>WT_50</th>\n",
       "      <th>CHOL_50</th>\n",
       "      <th>SBP_62</th>\n",
       "      <th>DBP_62</th>\n",
       "      <th>CHOL_62</th>\n",
       "      <th>WT_62</th>\n",
       "      <th>MD_50_1</th>\n",
       "      <th>...</th>\n",
       "      <th>IHD_DX_2</th>\n",
       "      <th>IHD_DX_3</th>\n",
       "      <th>IHD_DX_4</th>\n",
       "      <th>IHD_DX_5</th>\n",
       "      <th>IHD_DX_6</th>\n",
       "      <th>IHD_DX_7</th>\n",
       "      <th>IHD_DX_9</th>\n",
       "      <th>DEATH</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.387755</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.701299</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.197531</td>\n",
       "      <td>0.414286</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>0.311594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.341270</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.554545</td>\n",
       "      <td>0.376812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.469388</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.730159</td>\n",
       "      <td>0.419753</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0.338462</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.122449</td>\n",
       "      <td>-0.060870</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.547619</td>\n",
       "      <td>0.419753</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.710145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.387755</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.935065</td>\n",
       "      <td>1.079365</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.623188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.180247</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.734694</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.103175</td>\n",
       "      <td>0.491358</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.477273</td>\n",
       "      <td>0.159420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.510204</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>0.486364</td>\n",
       "      <td>0.224638</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.510204</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.492064</td>\n",
       "      <td>0.271605</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.369231</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.442029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.448980</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.753968</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.840580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.320988</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.640909</td>\n",
       "      <td>0.398551</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.301587</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>0.376812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.204082</td>\n",
       "      <td>0.095652</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.507937</td>\n",
       "      <td>0.320988</td>\n",
       "      <td>0.342857</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.481818</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.510204</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.269841</td>\n",
       "      <td>0.279012</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.276923</td>\n",
       "      <td>0.459091</td>\n",
       "      <td>0.275362</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.836735</td>\n",
       "      <td>0.373913</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.382716</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.036232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.467532</td>\n",
       "      <td>0.198413</td>\n",
       "      <td>0.279012</td>\n",
       "      <td>0.342857</td>\n",
       "      <td>-0.061538</td>\n",
       "      <td>0.513636</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.516049</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.481818</td>\n",
       "      <td>0.297101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.234783</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.706349</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.581818</td>\n",
       "      <td>0.746377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.597403</td>\n",
       "      <td>0.103175</td>\n",
       "      <td>0.279012</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>0.646154</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.036232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.510204</td>\n",
       "      <td>0.113043</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.365079</td>\n",
       "      <td>0.316049</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.504545</td>\n",
       "      <td>0.536232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.693878</td>\n",
       "      <td>0.460869</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.246032</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.531818</td>\n",
       "      <td>0.253623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.469388</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.269841</td>\n",
       "      <td>0.148148</td>\n",
       "      <td>0.385714</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>0.224638</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.448980</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.197531</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>0.322727</td>\n",
       "      <td>0.840580</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.306122</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.650794</td>\n",
       "      <td>0.471605</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.845455</td>\n",
       "      <td>0.442029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.204082</td>\n",
       "      <td>0.078261</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.412699</td>\n",
       "      <td>0.419753</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.645455</td>\n",
       "      <td>0.507246</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.163265</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.623377</td>\n",
       "      <td>0.634921</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>0.414286</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.690909</td>\n",
       "      <td>0.623188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.269565</td>\n",
       "      <td>0.610390</td>\n",
       "      <td>0.444445</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.672727</td>\n",
       "      <td>0.427536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.653061</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>0.170370</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.390909</td>\n",
       "      <td>0.195652</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>1.324675</td>\n",
       "      <td>0.698413</td>\n",
       "      <td>0.197531</td>\n",
       "      <td>1.035714</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>-0.040909</td>\n",
       "      <td>0.405797</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.040816</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.658730</td>\n",
       "      <td>0.271605</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.673913</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.530612</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.396825</td>\n",
       "      <td>0.348148</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.686364</td>\n",
       "      <td>0.405797</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.061224</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>-0.024691</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.326087</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.530612</td>\n",
       "      <td>0.113043</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.246032</td>\n",
       "      <td>0.320988</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.289855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.436508</td>\n",
       "      <td>0.274074</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.184615</td>\n",
       "      <td>0.559091</td>\n",
       "      <td>0.485507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.693878</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>0.507937</td>\n",
       "      <td>0.419753</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.504545</td>\n",
       "      <td>0.681159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.591837</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.481818</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.320988</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.468182</td>\n",
       "      <td>0.318841</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AGE_50    SBP_50    DBP_50     WT_50   CHOL_50    SBP_62    DBP_62  \\\n",
       "0   0.387755  0.304348  0.701299  0.277778  0.197531  0.414286  0.430769   \n",
       "1   0.551020  0.217391  0.545455  0.341270  0.222222  0.535714  0.246154   \n",
       "2   0.469388  0.217391  0.545455  0.730159  0.419753  0.485714  0.338462   \n",
       "3   0.122449 -0.060870  0.285714  0.547619  0.419753  0.300000  0.246154   \n",
       "4   0.387755  0.600000  0.935065  1.079365  0.222222  0.535714  0.538462   \n",
       "5   0.551020  0.130435  0.415584  0.166667  0.180247  0.392857  0.230769   \n",
       "6   0.734694  0.260870  0.480519  0.103175  0.491358  0.500000  0.307692   \n",
       "7   0.510204  0.304348  0.675325  0.722222  0.222222  0.428571  0.461538   \n",
       "8   0.714286  0.260870  0.675325  0.428571  0.259259  0.714286  0.523077   \n",
       "9   0.510204  0.217391  0.571429  0.492064  0.271605  0.500000  0.369231   \n",
       "10  0.448980  0.173913  0.480519  0.753968  0.222222  0.500000  0.307692   \n",
       "11  0.551020  0.304348  0.480519  0.285714  0.320988  0.485714  0.246154   \n",
       "12  0.428571  0.217391  0.545455  0.301587  0.222222  0.714286  0.769231   \n",
       "13  0.204082  0.095652  0.545455  0.507937  0.320988  0.342857  0.307692   \n",
       "14  0.510204  0.304348  0.675325  0.269841  0.279012  0.628571  0.276923   \n",
       "15  0.836735  0.373913  0.545455  0.388889  0.382716  0.357143  0.153846   \n",
       "16  0.673469  0.200000  0.467532  0.198413  0.279012  0.342857 -0.061538   \n",
       "17  0.428571  0.173913  0.545455  0.285714  0.516049  0.642857  0.307692   \n",
       "18  0.571429  0.234783  0.727273  0.706349  0.296296  0.714286  0.923077   \n",
       "19  0.816327  0.304348  0.597403  0.103175  0.279012  0.685714  0.646154   \n",
       "20  0.510204  0.113043  0.545455  0.365079  0.316049  0.485714  0.430769   \n",
       "21  0.693878  0.460869  0.675325  0.246032  0.222222  1.142857  0.692308   \n",
       "22  0.469388  0.130435  0.480519  0.269841  0.148148  0.385714  0.461538   \n",
       "23  0.448980  0.217391  0.675325  0.777778  0.197531  0.500000  0.523077   \n",
       "24  0.306122  0.217391  0.545455  0.650794  0.471605  0.428571  0.307692   \n",
       "25  0.204082  0.078261  0.415584  0.412699  0.419753  0.464286  0.384615   \n",
       "26  0.163265  0.217391  0.623377  0.634921  0.488889  0.414286  0.430769   \n",
       "27  0.285714  0.269565  0.610390  0.444445  0.444444  0.428571  0.461538   \n",
       "28  0.653061  0.260870  0.675325  0.253968  0.170370  0.585714  0.615385   \n",
       "29  0.673469  0.913043  1.324675  0.698413  0.197531  1.035714  0.615385   \n",
       "30  0.040816  0.217391  0.675325  0.658730  0.271605  0.571429  0.692308   \n",
       "31  0.530612  0.130435  0.415584  0.396825  0.348148  0.585714  0.430769   \n",
       "32  0.061224  0.130435  0.415584  0.222222 -0.024691  0.300000  0.246154   \n",
       "33  0.530612  0.113043  0.480519  0.246032  0.320988  0.357143  0.230769   \n",
       "34  0.326531  0.043478  0.480519  0.436508  0.274074  0.392857  0.184615   \n",
       "35  0.693878  0.043478  0.337662  0.507937  0.419753  0.392857  0.230769   \n",
       "36  0.591837  0.086957  0.480519  0.277778  0.296296  0.285714  0.153846   \n",
       "37  0.326531  0.043478  0.415584  0.380952  0.320988  0.357143  0.400000   \n",
       "\n",
       "     CHOL_62     WT_62  MD_50_1  ...  IHD_DX_2  IHD_DX_3  IHD_DX_4  IHD_DX_5  \\\n",
       "0   0.254545  0.311594      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "1   0.554545  0.376812      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "2   0.254545  0.652174      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "3   0.727273  0.710145      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "4   0.500000  0.623188      0.0  ...       1.0       0.0       0.0       0.0   \n",
       "5   0.227273  0.137681      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "6   0.477273  0.159420      0.0  ...       1.0       0.0       0.0       0.0   \n",
       "7   0.250000  0.434783      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "8   0.486364  0.224638      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "9   0.409091  0.442029      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "10  0.350000  0.840580      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "11  0.640909  0.398551      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "12  0.840909  0.376812      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "13  0.481818  0.565217      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "14  0.459091  0.275362      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "15  0.350000  0.036232      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "16  0.513636  0.217391      0.0  ...       1.0       0.0       0.0       0.0   \n",
       "17  0.481818  0.297101      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "18  0.581818  0.746377      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "19  0.650000  0.036232      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "20  0.504545  0.536232      0.0  ...       1.0       0.0       0.0       0.0   \n",
       "21  0.531818  0.253623      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "22  0.254545  0.224638      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "23  0.322727  0.840580      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "24  0.845455  0.442029      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "25  0.645455  0.507246      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "26  0.690909  0.623188      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "27  0.672727  0.427536      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "28  0.390909  0.195652      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "29 -0.040909  0.405797      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "30  0.550000  0.673913      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "31  0.686364  0.405797      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "32  0.227273  0.326087      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "33  0.363636  0.289855      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "34  0.559091  0.485507      0.0  ...       1.0       0.0       0.0       0.0   \n",
       "35  0.504545  0.681159      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "36  0.481818  0.260870      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "37  0.468182  0.318841      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "\n",
       "    IHD_DX_6  IHD_DX_7  IHD_DX_9  DEATH  Label   Score  \n",
       "0        0.0       0.0       0.0      0      1  0.6947  \n",
       "1        0.0       0.0       0.0      0      0  0.7062  \n",
       "2        0.0       0.0       0.0      0      1  0.5071  \n",
       "3        0.0       0.0       0.0      0      0  0.7558  \n",
       "4        0.0       0.0       0.0      0      1  0.5983  \n",
       "5        0.0       0.0       0.0      1      0  0.5885  \n",
       "6        0.0       0.0       0.0      1      1  0.7576  \n",
       "7        0.0       0.0       0.0      1      0  0.5543  \n",
       "8        1.0       0.0       0.0      0      0  0.5986  \n",
       "9        0.0       0.0       0.0      0      1  0.5746  \n",
       "10       0.0       0.0       0.0      0      0  0.5631  \n",
       "11       0.0       0.0       0.0      0      1  0.5867  \n",
       "12       0.0       0.0       0.0      0      0  0.7675  \n",
       "13       0.0       0.0       0.0      0      0  0.6950  \n",
       "14       1.0       0.0       0.0      0      0  0.7258  \n",
       "15       0.0       0.0       0.0      1      0  0.5708  \n",
       "16       0.0       0.0       0.0      1      1  0.6170  \n",
       "17       0.0       0.0       0.0      0      0  0.5925  \n",
       "18       0.0       0.0       0.0      0      1  0.5484  \n",
       "19       0.0       0.0       0.0      1      0  0.5453  \n",
       "20       0.0       0.0       0.0      0      1  0.6472  \n",
       "21       0.0       0.0       0.0      0      1  0.6041  \n",
       "22       0.0       0.0       0.0      0      1  0.6004  \n",
       "23       0.0       0.0       0.0      0      0  0.5166  \n",
       "24       1.0       0.0       0.0      0      0  0.6591  \n",
       "25       0.0       0.0       0.0      0      0  0.6051  \n",
       "26       1.0       0.0       0.0      0      0  0.7477  \n",
       "27       0.0       0.0       0.0      0      0  0.6362  \n",
       "28       1.0       0.0       0.0      0      0  0.5623  \n",
       "29       0.0       0.0       0.0      1      1  0.5459  \n",
       "30       0.0       0.0       0.0      0      0  0.6470  \n",
       "31       1.0       0.0       0.0      1      1  0.5480  \n",
       "32       0.0       0.0       0.0      0      1  0.6180  \n",
       "33       0.0       0.0       0.0      0      1  0.5581  \n",
       "34       0.0       0.0       0.0      0      1  0.5574  \n",
       "35       0.0       0.0       0.0      0      1  0.6094  \n",
       "36       0.0       0.0       0.0      0      1  0.5771  \n",
       "37       0.0       0.0       0.0      0      0  0.6121  \n",
       "\n",
       "[38 rows x 56 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_model(tuned_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3fb4f66b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table id=\"T_c1276\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c1276_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_c1276_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_c1276_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_c1276_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_c1276_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_c1276_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_c1276_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_c1276_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_c1276_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row0\" class=\"row_heading level0 row0\" >rf</th>\n",
       "      <td id=\"T_c1276_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_c1276_row0_col1\" class=\"data row0 col1\" >0.6767</td>\n",
       "      <td id=\"T_c1276_row0_col2\" class=\"data row0 col2\" >0.7313</td>\n",
       "      <td id=\"T_c1276_row0_col3\" class=\"data row0 col3\" >0.3733</td>\n",
       "      <td id=\"T_c1276_row0_col4\" class=\"data row0 col4\" >0.5067</td>\n",
       "      <td id=\"T_c1276_row0_col5\" class=\"data row0 col5\" >0.4120</td>\n",
       "      <td id=\"T_c1276_row0_col6\" class=\"data row0 col6\" >0.2181</td>\n",
       "      <td id=\"T_c1276_row0_col7\" class=\"data row0 col7\" >0.2319</td>\n",
       "      <td id=\"T_c1276_row0_col8\" class=\"data row0 col8\" >0.0410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row1\" class=\"row_heading level0 row1\" >et</th>\n",
       "      <td id=\"T_c1276_row1_col0\" class=\"data row1 col0\" >Extra Trees Classifier</td>\n",
       "      <td id=\"T_c1276_row1_col1\" class=\"data row1 col1\" >0.6700</td>\n",
       "      <td id=\"T_c1276_row1_col2\" class=\"data row1 col2\" >0.6782</td>\n",
       "      <td id=\"T_c1276_row1_col3\" class=\"data row1 col3\" >0.3167</td>\n",
       "      <td id=\"T_c1276_row1_col4\" class=\"data row1 col4\" >0.4817</td>\n",
       "      <td id=\"T_c1276_row1_col5\" class=\"data row1 col5\" >0.3549</td>\n",
       "      <td id=\"T_c1276_row1_col6\" class=\"data row1 col6\" >0.1720</td>\n",
       "      <td id=\"T_c1276_row1_col7\" class=\"data row1 col7\" >0.1790</td>\n",
       "      <td id=\"T_c1276_row1_col8\" class=\"data row1 col8\" >0.0350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row2\" class=\"row_heading level0 row2\" >lightgbm</th>\n",
       "      <td id=\"T_c1276_row2_col0\" class=\"data row2 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_c1276_row2_col1\" class=\"data row2 col1\" >0.6646</td>\n",
       "      <td id=\"T_c1276_row2_col2\" class=\"data row2 col2\" >0.6855</td>\n",
       "      <td id=\"T_c1276_row2_col3\" class=\"data row2 col3\" >0.4500</td>\n",
       "      <td id=\"T_c1276_row2_col4\" class=\"data row2 col4\" >0.5083</td>\n",
       "      <td id=\"T_c1276_row2_col5\" class=\"data row2 col5\" >0.4616</td>\n",
       "      <td id=\"T_c1276_row2_col6\" class=\"data row2 col6\" >0.2252</td>\n",
       "      <td id=\"T_c1276_row2_col7\" class=\"data row2 col7\" >0.2339</td>\n",
       "      <td id=\"T_c1276_row2_col8\" class=\"data row2 col8\" >0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row3\" class=\"row_heading level0 row3\" >dummy</th>\n",
       "      <td id=\"T_c1276_row3_col0\" class=\"data row3 col0\" >Dummy Classifier</td>\n",
       "      <td id=\"T_c1276_row3_col1\" class=\"data row3 col1\" >0.6646</td>\n",
       "      <td id=\"T_c1276_row3_col2\" class=\"data row3 col2\" >0.5000</td>\n",
       "      <td id=\"T_c1276_row3_col3\" class=\"data row3 col3\" >0.0000</td>\n",
       "      <td id=\"T_c1276_row3_col4\" class=\"data row3 col4\" >0.0000</td>\n",
       "      <td id=\"T_c1276_row3_col5\" class=\"data row3 col5\" >0.0000</td>\n",
       "      <td id=\"T_c1276_row3_col6\" class=\"data row3 col6\" >0.0000</td>\n",
       "      <td id=\"T_c1276_row3_col7\" class=\"data row3 col7\" >0.0000</td>\n",
       "      <td id=\"T_c1276_row3_col8\" class=\"data row3 col8\" >0.0040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row4\" class=\"row_heading level0 row4\" >ada</th>\n",
       "      <td id=\"T_c1276_row4_col0\" class=\"data row4 col0\" >Ada Boost Classifier</td>\n",
       "      <td id=\"T_c1276_row4_col1\" class=\"data row4 col1\" >0.6637</td>\n",
       "      <td id=\"T_c1276_row4_col2\" class=\"data row4 col2\" >0.6613</td>\n",
       "      <td id=\"T_c1276_row4_col3\" class=\"data row4 col3\" >0.5100</td>\n",
       "      <td id=\"T_c1276_row4_col4\" class=\"data row4 col4\" >0.5100</td>\n",
       "      <td id=\"T_c1276_row4_col5\" class=\"data row4 col5\" >0.4874</td>\n",
       "      <td id=\"T_c1276_row4_col6\" class=\"data row4 col6\" >0.2481</td>\n",
       "      <td id=\"T_c1276_row4_col7\" class=\"data row4 col7\" >0.2565</td>\n",
       "      <td id=\"T_c1276_row4_col8\" class=\"data row4 col8\" >0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row5\" class=\"row_heading level0 row5\" >lr</th>\n",
       "      <td id=\"T_c1276_row5_col0\" class=\"data row5 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_c1276_row5_col1\" class=\"data row5 col1\" >0.6575</td>\n",
       "      <td id=\"T_c1276_row5_col2\" class=\"data row5 col2\" >0.7005</td>\n",
       "      <td id=\"T_c1276_row5_col3\" class=\"data row5 col3\" >0.5900</td>\n",
       "      <td id=\"T_c1276_row5_col4\" class=\"data row5 col4\" >0.4892</td>\n",
       "      <td id=\"T_c1276_row5_col5\" class=\"data row5 col5\" >0.5267</td>\n",
       "      <td id=\"T_c1276_row5_col6\" class=\"data row5 col6\" >0.2635</td>\n",
       "      <td id=\"T_c1276_row5_col7\" class=\"data row5 col7\" >0.2747</td>\n",
       "      <td id=\"T_c1276_row5_col8\" class=\"data row5 col8\" >0.4380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row6\" class=\"row_heading level0 row6\" >svm</th>\n",
       "      <td id=\"T_c1276_row6_col0\" class=\"data row6 col0\" >SVM - Linear Kernel</td>\n",
       "      <td id=\"T_c1276_row6_col1\" class=\"data row6 col1\" >0.6446</td>\n",
       "      <td id=\"T_c1276_row6_col2\" class=\"data row6 col2\" >0.0000</td>\n",
       "      <td id=\"T_c1276_row6_col3\" class=\"data row6 col3\" >0.3967</td>\n",
       "      <td id=\"T_c1276_row6_col4\" class=\"data row6 col4\" >0.4761</td>\n",
       "      <td id=\"T_c1276_row6_col5\" class=\"data row6 col5\" >0.4061</td>\n",
       "      <td id=\"T_c1276_row6_col6\" class=\"data row6 col6\" >0.1701</td>\n",
       "      <td id=\"T_c1276_row6_col7\" class=\"data row6 col7\" >0.1824</td>\n",
       "      <td id=\"T_c1276_row6_col8\" class=\"data row6 col8\" >0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row7\" class=\"row_heading level0 row7\" >qda</th>\n",
       "      <td id=\"T_c1276_row7_col0\" class=\"data row7 col0\" >Quadratic Discriminant Analysis</td>\n",
       "      <td id=\"T_c1276_row7_col1\" class=\"data row7 col1\" >0.6438</td>\n",
       "      <td id=\"T_c1276_row7_col2\" class=\"data row7 col2\" >0.5892</td>\n",
       "      <td id=\"T_c1276_row7_col3\" class=\"data row7 col3\" >0.4167</td>\n",
       "      <td id=\"T_c1276_row7_col4\" class=\"data row7 col4\" >0.5271</td>\n",
       "      <td id=\"T_c1276_row7_col5\" class=\"data row7 col5\" >0.4324</td>\n",
       "      <td id=\"T_c1276_row7_col6\" class=\"data row7 col6\" >0.1864</td>\n",
       "      <td id=\"T_c1276_row7_col7\" class=\"data row7 col7\" >0.2034</td>\n",
       "      <td id=\"T_c1276_row7_col8\" class=\"data row7 col8\" >0.0060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row8\" class=\"row_heading level0 row8\" >ridge</th>\n",
       "      <td id=\"T_c1276_row8_col0\" class=\"data row8 col0\" >Ridge Classifier</td>\n",
       "      <td id=\"T_c1276_row8_col1\" class=\"data row8 col1\" >0.6375</td>\n",
       "      <td id=\"T_c1276_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
       "      <td id=\"T_c1276_row8_col3\" class=\"data row8 col3\" >0.5500</td>\n",
       "      <td id=\"T_c1276_row8_col4\" class=\"data row8 col4\" >0.4683</td>\n",
       "      <td id=\"T_c1276_row8_col5\" class=\"data row8 col5\" >0.4932</td>\n",
       "      <td id=\"T_c1276_row8_col6\" class=\"data row8 col6\" >0.2191</td>\n",
       "      <td id=\"T_c1276_row8_col7\" class=\"data row8 col7\" >0.2293</td>\n",
       "      <td id=\"T_c1276_row8_col8\" class=\"data row8 col8\" >0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row9\" class=\"row_heading level0 row9\" >gbc</th>\n",
       "      <td id=\"T_c1276_row9_col0\" class=\"data row9 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_c1276_row9_col1\" class=\"data row9 col1\" >0.6371</td>\n",
       "      <td id=\"T_c1276_row9_col2\" class=\"data row9 col2\" >0.6521</td>\n",
       "      <td id=\"T_c1276_row9_col3\" class=\"data row9 col3\" >0.3933</td>\n",
       "      <td id=\"T_c1276_row9_col4\" class=\"data row9 col4\" >0.5105</td>\n",
       "      <td id=\"T_c1276_row9_col5\" class=\"data row9 col5\" >0.4112</td>\n",
       "      <td id=\"T_c1276_row9_col6\" class=\"data row9 col6\" >0.1671</td>\n",
       "      <td id=\"T_c1276_row9_col7\" class=\"data row9 col7\" >0.1827</td>\n",
       "      <td id=\"T_c1276_row9_col8\" class=\"data row9 col8\" >0.0180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row10\" class=\"row_heading level0 row10\" >lda</th>\n",
       "      <td id=\"T_c1276_row10_col0\" class=\"data row10 col0\" >Linear Discriminant Analysis</td>\n",
       "      <td id=\"T_c1276_row10_col1\" class=\"data row10 col1\" >0.6308</td>\n",
       "      <td id=\"T_c1276_row10_col2\" class=\"data row10 col2\" >0.6663</td>\n",
       "      <td id=\"T_c1276_row10_col3\" class=\"data row10 col3\" >0.5267</td>\n",
       "      <td id=\"T_c1276_row10_col4\" class=\"data row10 col4\" >0.4475</td>\n",
       "      <td id=\"T_c1276_row10_col5\" class=\"data row10 col5\" >0.4649</td>\n",
       "      <td id=\"T_c1276_row10_col6\" class=\"data row10 col6\" >0.1961</td>\n",
       "      <td id=\"T_c1276_row10_col7\" class=\"data row10 col7\" >0.2063</td>\n",
       "      <td id=\"T_c1276_row10_col8\" class=\"data row10 col8\" >0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row11\" class=\"row_heading level0 row11\" >dt</th>\n",
       "      <td id=\"T_c1276_row11_col0\" class=\"data row11 col0\" >Decision Tree Classifier</td>\n",
       "      <td id=\"T_c1276_row11_col1\" class=\"data row11 col1\" >0.6175</td>\n",
       "      <td id=\"T_c1276_row11_col2\" class=\"data row11 col2\" >0.5785</td>\n",
       "      <td id=\"T_c1276_row11_col3\" class=\"data row11 col3\" >0.4533</td>\n",
       "      <td id=\"T_c1276_row11_col4\" class=\"data row11 col4\" >0.4706</td>\n",
       "      <td id=\"T_c1276_row11_col5\" class=\"data row11 col5\" >0.4376</td>\n",
       "      <td id=\"T_c1276_row11_col6\" class=\"data row11 col6\" >0.1619</td>\n",
       "      <td id=\"T_c1276_row11_col7\" class=\"data row11 col7\" >0.1665</td>\n",
       "      <td id=\"T_c1276_row11_col8\" class=\"data row11 col8\" >0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row12\" class=\"row_heading level0 row12\" >knn</th>\n",
       "      <td id=\"T_c1276_row12_col0\" class=\"data row12 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_c1276_row12_col1\" class=\"data row12 col1\" >0.5588</td>\n",
       "      <td id=\"T_c1276_row12_col2\" class=\"data row12 col2\" >0.6102</td>\n",
       "      <td id=\"T_c1276_row12_col3\" class=\"data row12 col3\" >0.5700</td>\n",
       "      <td id=\"T_c1276_row12_col4\" class=\"data row12 col4\" >0.3811</td>\n",
       "      <td id=\"T_c1276_row12_col5\" class=\"data row12 col5\" >0.4443</td>\n",
       "      <td id=\"T_c1276_row12_col6\" class=\"data row12 col6\" >0.1016</td>\n",
       "      <td id=\"T_c1276_row12_col7\" class=\"data row12 col7\" >0.1221</td>\n",
       "      <td id=\"T_c1276_row12_col8\" class=\"data row12 col8\" >0.2380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1276_level0_row13\" class=\"row_heading level0 row13\" >nb</th>\n",
       "      <td id=\"T_c1276_row13_col0\" class=\"data row13 col0\" >Naive Bayes</td>\n",
       "      <td id=\"T_c1276_row13_col1\" class=\"data row13 col1\" >0.4658</td>\n",
       "      <td id=\"T_c1276_row13_col2\" class=\"data row13 col2\" >0.5864</td>\n",
       "      <td id=\"T_c1276_row13_col3\" class=\"data row13 col3\" >0.9400</td>\n",
       "      <td id=\"T_c1276_row13_col4\" class=\"data row13 col4\" >0.3815</td>\n",
       "      <td id=\"T_c1276_row13_col5\" class=\"data row13 col5\" >0.5421</td>\n",
       "      <td id=\"T_c1276_row13_col6\" class=\"data row13 col6\" >0.1226</td>\n",
       "      <td id=\"T_c1276_row13_col7\" class=\"data row13 col7\" >0.2110</td>\n",
       "      <td id=\"T_c1276_row13_col8\" class=\"data row13 col8\" >0.0040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x279fc426280>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best = compare_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2ce22cb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_2cce3_row10_col0, #T_2cce3_row10_col1, #T_2cce3_row10_col2, #T_2cce3_row10_col3, #T_2cce3_row10_col4, #T_2cce3_row10_col5, #T_2cce3_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_2cce3\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2cce3_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_2cce3_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_2cce3_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_2cce3_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_2cce3_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_2cce3_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_2cce3_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_2cce3_row0_col0\" class=\"data row0 col0\" >0.8125</td>\n",
       "      <td id=\"T_2cce3_row0_col1\" class=\"data row0 col1\" >0.7545</td>\n",
       "      <td id=\"T_2cce3_row0_col2\" class=\"data row0 col2\" >0.6000</td>\n",
       "      <td id=\"T_2cce3_row0_col3\" class=\"data row0 col3\" >0.7500</td>\n",
       "      <td id=\"T_2cce3_row0_col4\" class=\"data row0 col4\" >0.6667</td>\n",
       "      <td id=\"T_2cce3_row0_col5\" class=\"data row0 col5\" >0.5385</td>\n",
       "      <td id=\"T_2cce3_row0_col6\" class=\"data row0 col6\" >0.5449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_2cce3_row1_col0\" class=\"data row1 col0\" >0.7500</td>\n",
       "      <td id=\"T_2cce3_row1_col1\" class=\"data row1 col1\" >0.7667</td>\n",
       "      <td id=\"T_2cce3_row1_col2\" class=\"data row1 col2\" >0.5000</td>\n",
       "      <td id=\"T_2cce3_row1_col3\" class=\"data row1 col3\" >0.7500</td>\n",
       "      <td id=\"T_2cce3_row1_col4\" class=\"data row1 col4\" >0.6000</td>\n",
       "      <td id=\"T_2cce3_row1_col5\" class=\"data row1 col5\" >0.4286</td>\n",
       "      <td id=\"T_2cce3_row1_col6\" class=\"data row1 col6\" >0.4472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_2cce3_row2_col0\" class=\"data row2 col0\" >0.7333</td>\n",
       "      <td id=\"T_2cce3_row2_col1\" class=\"data row2 col1\" >0.7200</td>\n",
       "      <td id=\"T_2cce3_row2_col2\" class=\"data row2 col2\" >0.6000</td>\n",
       "      <td id=\"T_2cce3_row2_col3\" class=\"data row2 col3\" >0.6000</td>\n",
       "      <td id=\"T_2cce3_row2_col4\" class=\"data row2 col4\" >0.6000</td>\n",
       "      <td id=\"T_2cce3_row2_col5\" class=\"data row2 col5\" >0.4000</td>\n",
       "      <td id=\"T_2cce3_row2_col6\" class=\"data row2 col6\" >0.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_2cce3_row3_col0\" class=\"data row3 col0\" >0.8000</td>\n",
       "      <td id=\"T_2cce3_row3_col1\" class=\"data row3 col1\" >0.8600</td>\n",
       "      <td id=\"T_2cce3_row3_col2\" class=\"data row3 col2\" >0.6000</td>\n",
       "      <td id=\"T_2cce3_row3_col3\" class=\"data row3 col3\" >0.7500</td>\n",
       "      <td id=\"T_2cce3_row3_col4\" class=\"data row3 col4\" >0.6667</td>\n",
       "      <td id=\"T_2cce3_row3_col5\" class=\"data row3 col5\" >0.5263</td>\n",
       "      <td id=\"T_2cce3_row3_col6\" class=\"data row3 col6\" >0.5330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_2cce3_row4_col0\" class=\"data row4 col0\" >0.5333</td>\n",
       "      <td id=\"T_2cce3_row4_col1\" class=\"data row4 col1\" >0.6700</td>\n",
       "      <td id=\"T_2cce3_row4_col2\" class=\"data row4 col2\" >0.8000</td>\n",
       "      <td id=\"T_2cce3_row4_col3\" class=\"data row4 col3\" >0.4000</td>\n",
       "      <td id=\"T_2cce3_row4_col4\" class=\"data row4 col4\" >0.5333</td>\n",
       "      <td id=\"T_2cce3_row4_col5\" class=\"data row4 col5\" >0.1600</td>\n",
       "      <td id=\"T_2cce3_row4_col6\" class=\"data row4 col6\" >0.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_2cce3_row5_col0\" class=\"data row5 col0\" >0.6667</td>\n",
       "      <td id=\"T_2cce3_row5_col1\" class=\"data row5 col1\" >0.8000</td>\n",
       "      <td id=\"T_2cce3_row5_col2\" class=\"data row5 col2\" >0.6000</td>\n",
       "      <td id=\"T_2cce3_row5_col3\" class=\"data row5 col3\" >0.5000</td>\n",
       "      <td id=\"T_2cce3_row5_col4\" class=\"data row5 col4\" >0.5455</td>\n",
       "      <td id=\"T_2cce3_row5_col5\" class=\"data row5 col5\" >0.2857</td>\n",
       "      <td id=\"T_2cce3_row5_col6\" class=\"data row5 col6\" >0.2887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_2cce3_row6_col0\" class=\"data row6 col0\" >0.8667</td>\n",
       "      <td id=\"T_2cce3_row6_col1\" class=\"data row6 col1\" >0.9600</td>\n",
       "      <td id=\"T_2cce3_row6_col2\" class=\"data row6 col2\" >0.8000</td>\n",
       "      <td id=\"T_2cce3_row6_col3\" class=\"data row6 col3\" >0.8000</td>\n",
       "      <td id=\"T_2cce3_row6_col4\" class=\"data row6 col4\" >0.8000</td>\n",
       "      <td id=\"T_2cce3_row6_col5\" class=\"data row6 col5\" >0.7000</td>\n",
       "      <td id=\"T_2cce3_row6_col6\" class=\"data row6 col6\" >0.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_2cce3_row7_col0\" class=\"data row7 col0\" >0.8000</td>\n",
       "      <td id=\"T_2cce3_row7_col1\" class=\"data row7 col1\" >0.8400</td>\n",
       "      <td id=\"T_2cce3_row7_col2\" class=\"data row7 col2\" >0.8000</td>\n",
       "      <td id=\"T_2cce3_row7_col3\" class=\"data row7 col3\" >0.6667</td>\n",
       "      <td id=\"T_2cce3_row7_col4\" class=\"data row7 col4\" >0.7273</td>\n",
       "      <td id=\"T_2cce3_row7_col5\" class=\"data row7 col5\" >0.5714</td>\n",
       "      <td id=\"T_2cce3_row7_col6\" class=\"data row7 col6\" >0.5774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_2cce3_row8_col0\" class=\"data row8 col0\" >0.6667</td>\n",
       "      <td id=\"T_2cce3_row8_col1\" class=\"data row8 col1\" >0.5700</td>\n",
       "      <td id=\"T_2cce3_row8_col2\" class=\"data row8 col2\" >0.6000</td>\n",
       "      <td id=\"T_2cce3_row8_col3\" class=\"data row8 col3\" >0.5000</td>\n",
       "      <td id=\"T_2cce3_row8_col4\" class=\"data row8 col4\" >0.5455</td>\n",
       "      <td id=\"T_2cce3_row8_col5\" class=\"data row8 col5\" >0.2857</td>\n",
       "      <td id=\"T_2cce3_row8_col6\" class=\"data row8 col6\" >0.2887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_2cce3_row9_col0\" class=\"data row9 col0\" >0.6000</td>\n",
       "      <td id=\"T_2cce3_row9_col1\" class=\"data row9 col1\" >0.6200</td>\n",
       "      <td id=\"T_2cce3_row9_col2\" class=\"data row9 col2\" >0.4000</td>\n",
       "      <td id=\"T_2cce3_row9_col3\" class=\"data row9 col3\" >0.4000</td>\n",
       "      <td id=\"T_2cce3_row9_col4\" class=\"data row9 col4\" >0.4000</td>\n",
       "      <td id=\"T_2cce3_row9_col5\" class=\"data row9 col5\" >0.1000</td>\n",
       "      <td id=\"T_2cce3_row9_col6\" class=\"data row9 col6\" >0.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_2cce3_row10_col0\" class=\"data row10 col0\" >0.7229</td>\n",
       "      <td id=\"T_2cce3_row10_col1\" class=\"data row10 col1\" >0.7561</td>\n",
       "      <td id=\"T_2cce3_row10_col2\" class=\"data row10 col2\" >0.6300</td>\n",
       "      <td id=\"T_2cce3_row10_col3\" class=\"data row10 col3\" >0.6117</td>\n",
       "      <td id=\"T_2cce3_row10_col4\" class=\"data row10 col4\" >0.6085</td>\n",
       "      <td id=\"T_2cce3_row10_col5\" class=\"data row10 col5\" >0.3996</td>\n",
       "      <td id=\"T_2cce3_row10_col6\" class=\"data row10 col6\" >0.4080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2cce3_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_2cce3_row11_col0\" class=\"data row11 col0\" >0.0994</td>\n",
       "      <td id=\"T_2cce3_row11_col1\" class=\"data row11 col1\" >0.1111</td>\n",
       "      <td id=\"T_2cce3_row11_col2\" class=\"data row11 col2\" >0.1269</td>\n",
       "      <td id=\"T_2cce3_row11_col3\" class=\"data row11 col3\" >0.1451</td>\n",
       "      <td id=\"T_2cce3_row11_col4\" class=\"data row11 col4\" >0.1071</td>\n",
       "      <td id=\"T_2cce3_row11_col5\" class=\"data row11 col5\" >0.1814</td>\n",
       "      <td id=\"T_2cce3_row11_col6\" class=\"data row11 col6\" >0.1779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x27a04691940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_best = tune_model(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad34c736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_best, plot =\"pr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9c08ef4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_best = finalize_model(tuned_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ad76ddfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_3760f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_3760f_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_3760f_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_3760f_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_3760f_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_3760f_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_3760f_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_3760f_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_3760f_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_3760f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_3760f_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_3760f_row0_col1\" class=\"data row0 col1\" >0.8158</td>\n",
       "      <td id=\"T_3760f_row0_col2\" class=\"data row0 col2\" >0.8688</td>\n",
       "      <td id=\"T_3760f_row0_col3\" class=\"data row0 col3\" >0.6250</td>\n",
       "      <td id=\"T_3760f_row0_col4\" class=\"data row0 col4\" >0.5556</td>\n",
       "      <td id=\"T_3760f_row0_col5\" class=\"data row0 col5\" >0.5882</td>\n",
       "      <td id=\"T_3760f_row0_col6\" class=\"data row0 col6\" >0.4701</td>\n",
       "      <td id=\"T_3760f_row0_col7\" class=\"data row0 col7\" >0.4715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x27a0481c8b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>AGE_50</th>\n",
       "      <th>SBP_50</th>\n",
       "      <th>DBP_50</th>\n",
       "      <th>WT_50</th>\n",
       "      <th>CHOL_50</th>\n",
       "      <th>SBP_62</th>\n",
       "      <th>DBP_62</th>\n",
       "      <th>CHOL_62</th>\n",
       "      <th>WT_62</th>\n",
       "      <th>MD_50_1</th>\n",
       "      <th>...</th>\n",
       "      <th>IHD_DX_2</th>\n",
       "      <th>IHD_DX_3</th>\n",
       "      <th>IHD_DX_4</th>\n",
       "      <th>IHD_DX_5</th>\n",
       "      <th>IHD_DX_6</th>\n",
       "      <th>IHD_DX_7</th>\n",
       "      <th>IHD_DX_9</th>\n",
       "      <th>DEATH</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.387755</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.701299</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.197531</td>\n",
       "      <td>0.414286</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>0.311594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.341270</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.554545</td>\n",
       "      <td>0.376812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.469388</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.730159</td>\n",
       "      <td>0.419753</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0.338462</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.122449</td>\n",
       "      <td>-0.060870</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.547619</td>\n",
       "      <td>0.419753</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.710145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.387755</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.935065</td>\n",
       "      <td>1.079365</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.623188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.180247</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.137681</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.734694</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.103175</td>\n",
       "      <td>0.491358</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.477273</td>\n",
       "      <td>0.159420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.510204</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>0.486364</td>\n",
       "      <td>0.224638</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.510204</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.492064</td>\n",
       "      <td>0.271605</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.369231</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.442029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.448980</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.753968</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.840580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.320988</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.640909</td>\n",
       "      <td>0.398551</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.301587</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>0.376812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.204082</td>\n",
       "      <td>0.095652</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.507937</td>\n",
       "      <td>0.320988</td>\n",
       "      <td>0.342857</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.481818</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.510204</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.269841</td>\n",
       "      <td>0.279012</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.276923</td>\n",
       "      <td>0.459091</td>\n",
       "      <td>0.275362</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.836735</td>\n",
       "      <td>0.373913</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.382716</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.036232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.467532</td>\n",
       "      <td>0.198413</td>\n",
       "      <td>0.279012</td>\n",
       "      <td>0.342857</td>\n",
       "      <td>-0.061538</td>\n",
       "      <td>0.513636</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.516049</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.481818</td>\n",
       "      <td>0.297101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.234783</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.706349</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.581818</td>\n",
       "      <td>0.746377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.597403</td>\n",
       "      <td>0.103175</td>\n",
       "      <td>0.279012</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>0.646154</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.036232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.510204</td>\n",
       "      <td>0.113043</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.365079</td>\n",
       "      <td>0.316049</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.504545</td>\n",
       "      <td>0.536232</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.693878</td>\n",
       "      <td>0.460869</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.246032</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.531818</td>\n",
       "      <td>0.253623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.469388</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.269841</td>\n",
       "      <td>0.148148</td>\n",
       "      <td>0.385714</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>0.224638</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.448980</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.197531</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>0.322727</td>\n",
       "      <td>0.840580</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.306122</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.650794</td>\n",
       "      <td>0.471605</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.845455</td>\n",
       "      <td>0.442029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.204082</td>\n",
       "      <td>0.078261</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.412699</td>\n",
       "      <td>0.419753</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.645455</td>\n",
       "      <td>0.507246</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.163265</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.623377</td>\n",
       "      <td>0.634921</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>0.414286</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.690909</td>\n",
       "      <td>0.623188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.269565</td>\n",
       "      <td>0.610390</td>\n",
       "      <td>0.444445</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.672727</td>\n",
       "      <td>0.427536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.653061</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>0.170370</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.390909</td>\n",
       "      <td>0.195652</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>1.324675</td>\n",
       "      <td>0.698413</td>\n",
       "      <td>0.197531</td>\n",
       "      <td>1.035714</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>-0.040909</td>\n",
       "      <td>0.405797</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.040816</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.675325</td>\n",
       "      <td>0.658730</td>\n",
       "      <td>0.271605</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.673913</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.530612</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.396825</td>\n",
       "      <td>0.348148</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.686364</td>\n",
       "      <td>0.405797</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.061224</td>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>-0.024691</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.246154</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.326087</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.530612</td>\n",
       "      <td>0.113043</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.246032</td>\n",
       "      <td>0.320988</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.289855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.436508</td>\n",
       "      <td>0.274074</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.184615</td>\n",
       "      <td>0.559091</td>\n",
       "      <td>0.485507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.693878</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.337662</td>\n",
       "      <td>0.507937</td>\n",
       "      <td>0.419753</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.504545</td>\n",
       "      <td>0.681159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.591837</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.480519</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.481818</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.320988</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.468182</td>\n",
       "      <td>0.318841</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AGE_50    SBP_50    DBP_50     WT_50   CHOL_50    SBP_62    DBP_62  \\\n",
       "0   0.387755  0.304348  0.701299  0.277778  0.197531  0.414286  0.430769   \n",
       "1   0.551020  0.217391  0.545455  0.341270  0.222222  0.535714  0.246154   \n",
       "2   0.469388  0.217391  0.545455  0.730159  0.419753  0.485714  0.338462   \n",
       "3   0.122449 -0.060870  0.285714  0.547619  0.419753  0.300000  0.246154   \n",
       "4   0.387755  0.600000  0.935065  1.079365  0.222222  0.535714  0.538462   \n",
       "5   0.551020  0.130435  0.415584  0.166667  0.180247  0.392857  0.230769   \n",
       "6   0.734694  0.260870  0.480519  0.103175  0.491358  0.500000  0.307692   \n",
       "7   0.510204  0.304348  0.675325  0.722222  0.222222  0.428571  0.461538   \n",
       "8   0.714286  0.260870  0.675325  0.428571  0.259259  0.714286  0.523077   \n",
       "9   0.510204  0.217391  0.571429  0.492064  0.271605  0.500000  0.369231   \n",
       "10  0.448980  0.173913  0.480519  0.753968  0.222222  0.500000  0.307692   \n",
       "11  0.551020  0.304348  0.480519  0.285714  0.320988  0.485714  0.246154   \n",
       "12  0.428571  0.217391  0.545455  0.301587  0.222222  0.714286  0.769231   \n",
       "13  0.204082  0.095652  0.545455  0.507937  0.320988  0.342857  0.307692   \n",
       "14  0.510204  0.304348  0.675325  0.269841  0.279012  0.628571  0.276923   \n",
       "15  0.836735  0.373913  0.545455  0.388889  0.382716  0.357143  0.153846   \n",
       "16  0.673469  0.200000  0.467532  0.198413  0.279012  0.342857 -0.061538   \n",
       "17  0.428571  0.173913  0.545455  0.285714  0.516049  0.642857  0.307692   \n",
       "18  0.571429  0.234783  0.727273  0.706349  0.296296  0.714286  0.923077   \n",
       "19  0.816327  0.304348  0.597403  0.103175  0.279012  0.685714  0.646154   \n",
       "20  0.510204  0.113043  0.545455  0.365079  0.316049  0.485714  0.430769   \n",
       "21  0.693878  0.460869  0.675325  0.246032  0.222222  1.142857  0.692308   \n",
       "22  0.469388  0.130435  0.480519  0.269841  0.148148  0.385714  0.461538   \n",
       "23  0.448980  0.217391  0.675325  0.777778  0.197531  0.500000  0.523077   \n",
       "24  0.306122  0.217391  0.545455  0.650794  0.471605  0.428571  0.307692   \n",
       "25  0.204082  0.078261  0.415584  0.412699  0.419753  0.464286  0.384615   \n",
       "26  0.163265  0.217391  0.623377  0.634921  0.488889  0.414286  0.430769   \n",
       "27  0.285714  0.269565  0.610390  0.444445  0.444444  0.428571  0.461538   \n",
       "28  0.653061  0.260870  0.675325  0.253968  0.170370  0.585714  0.615385   \n",
       "29  0.673469  0.913043  1.324675  0.698413  0.197531  1.035714  0.615385   \n",
       "30  0.040816  0.217391  0.675325  0.658730  0.271605  0.571429  0.692308   \n",
       "31  0.530612  0.130435  0.415584  0.396825  0.348148  0.585714  0.430769   \n",
       "32  0.061224  0.130435  0.415584  0.222222 -0.024691  0.300000  0.246154   \n",
       "33  0.530612  0.113043  0.480519  0.246032  0.320988  0.357143  0.230769   \n",
       "34  0.326531  0.043478  0.480519  0.436508  0.274074  0.392857  0.184615   \n",
       "35  0.693878  0.043478  0.337662  0.507937  0.419753  0.392857  0.230769   \n",
       "36  0.591837  0.086957  0.480519  0.277778  0.296296  0.285714  0.153846   \n",
       "37  0.326531  0.043478  0.415584  0.380952  0.320988  0.357143  0.400000   \n",
       "\n",
       "     CHOL_62     WT_62  MD_50_1  ...  IHD_DX_2  IHD_DX_3  IHD_DX_4  IHD_DX_5  \\\n",
       "0   0.254545  0.311594      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "1   0.554545  0.376812      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "2   0.254545  0.652174      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "3   0.727273  0.710145      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "4   0.500000  0.623188      0.0  ...       1.0       0.0       0.0       0.0   \n",
       "5   0.227273  0.137681      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "6   0.477273  0.159420      0.0  ...       1.0       0.0       0.0       0.0   \n",
       "7   0.250000  0.434783      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "8   0.486364  0.224638      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "9   0.409091  0.442029      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "10  0.350000  0.840580      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "11  0.640909  0.398551      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "12  0.840909  0.376812      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "13  0.481818  0.565217      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "14  0.459091  0.275362      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "15  0.350000  0.036232      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "16  0.513636  0.217391      0.0  ...       1.0       0.0       0.0       0.0   \n",
       "17  0.481818  0.297101      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "18  0.581818  0.746377      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "19  0.650000  0.036232      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "20  0.504545  0.536232      0.0  ...       1.0       0.0       0.0       0.0   \n",
       "21  0.531818  0.253623      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "22  0.254545  0.224638      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "23  0.322727  0.840580      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "24  0.845455  0.442029      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "25  0.645455  0.507246      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "26  0.690909  0.623188      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "27  0.672727  0.427536      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "28  0.390909  0.195652      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "29 -0.040909  0.405797      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "30  0.550000  0.673913      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "31  0.686364  0.405797      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "32  0.227273  0.326087      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "33  0.363636  0.289855      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "34  0.559091  0.485507      0.0  ...       1.0       0.0       0.0       0.0   \n",
       "35  0.504545  0.681159      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "36  0.481818  0.260870      0.0  ...       0.0       0.0       0.0       0.0   \n",
       "37  0.468182  0.318841      1.0  ...       0.0       0.0       0.0       0.0   \n",
       "\n",
       "    IHD_DX_6  IHD_DX_7  IHD_DX_9  DEATH  Label   Score  \n",
       "0        0.0       0.0       0.0      0      0  0.7080  \n",
       "1        0.0       0.0       0.0      0      0  0.6679  \n",
       "2        0.0       0.0       0.0      0      0  0.7098  \n",
       "3        0.0       0.0       0.0      0      0  0.7165  \n",
       "4        0.0       0.0       0.0      0      0  0.7134  \n",
       "5        0.0       0.0       0.0      1      0  0.6679  \n",
       "6        0.0       0.0       0.0      1      1  0.7552  \n",
       "7        0.0       0.0       0.0      1      0  0.6974  \n",
       "8        1.0       0.0       0.0      0      1  0.7552  \n",
       "9        0.0       0.0       0.0      0      0  0.6974  \n",
       "10       0.0       0.0       0.0      0      0  0.7098  \n",
       "11       0.0       0.0       0.0      0      0  0.6679  \n",
       "12       0.0       0.0       0.0      0      0  0.7080  \n",
       "13       0.0       0.0       0.0      0      0  0.7165  \n",
       "14       1.0       0.0       0.0      0      0  0.6946  \n",
       "15       0.0       0.0       0.0      1      1  0.7552  \n",
       "16       0.0       0.0       0.0      1      1  0.7552  \n",
       "17       0.0       0.0       0.0      0      0  0.7080  \n",
       "18       0.0       0.0       0.0      0      0  0.6221  \n",
       "19       0.0       0.0       0.0      1      1  0.7552  \n",
       "20       0.0       0.0       0.0      0      0  0.7000  \n",
       "21       0.0       0.0       0.0      0      1  0.7552  \n",
       "22       0.0       0.0       0.0      0      0  0.7044  \n",
       "23       0.0       0.0       0.0      0      0  0.7098  \n",
       "24       1.0       0.0       0.0      0      0  0.7139  \n",
       "25       0.0       0.0       0.0      0      0  0.7165  \n",
       "26       1.0       0.0       0.0      0      0  0.7165  \n",
       "27       0.0       0.0       0.0      0      0  0.7139  \n",
       "28       1.0       0.0       0.0      0      1  0.5005  \n",
       "29       0.0       0.0       0.0      1      1  0.7552  \n",
       "30       0.0       0.0       0.0      0      0  0.7165  \n",
       "31       1.0       0.0       0.0      1      0  0.6946  \n",
       "32       0.0       0.0       0.0      0      0  0.7111  \n",
       "33       0.0       0.0       0.0      0      0  0.6946  \n",
       "34       0.0       0.0       0.0      0      0  0.7165  \n",
       "35       0.0       0.0       0.0      0      1  0.7498  \n",
       "36       0.0       0.0       0.0      0      0  0.5451  \n",
       "37       0.0       0.0       0.0      0      0  0.7111  \n",
       "\n",
       "[38 rows x 56 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_model(final_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e54b593b",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2b98d812",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>AGE_50</th>\n",
       "      <th>MD_50</th>\n",
       "      <th>SBP_50</th>\n",
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       "      <td>141</td>\n",
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       "      <td>65</td>\n",
       "      <td>171</td>\n",
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       "      <td>4</td>\n",
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       "      <td>66</td>\n",
       "      <td>183</td>\n",
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       "      <td>3</td>\n",
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       "      <td>88</td>\n",
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       "      <td>143</td>\n",
       "      <td>6</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.5005</td>\n",
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       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "      <td>61</td>\n",
       "      <td>120</td>\n",
       "      <td>317</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>68</td>\n",
       "      <td>248</td>\n",
       "      <td>118</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7111</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>138</td>\n",
       "      <td>100</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
       "      <td>296</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>135</td>\n",
       "      <td>95</td>\n",
       "      <td>286</td>\n",
       "      <td>199</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>85</td>\n",
       "      <td>70</td>\n",
       "      <td>134</td>\n",
       "      <td>220</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>85</td>\n",
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       "      <td>141</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>80</td>\n",
       "      <td>74</td>\n",
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       "      <td>69</td>\n",
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       "      <td>3</td>\n",
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       "      <td>174</td>\n",
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       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>135</td>\n",
       "      <td>80</td>\n",
       "      <td>69</td>\n",
       "      <td>155</td>\n",
       "      <td>290</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>84</td>\n",
       "      <td>238</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>141</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>170</td>\n",
       "      <td>98</td>\n",
       "      <td>221</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE_50  MD_50  SBP_50  DBP_50  HT_50  WT_50  CHOL_50  SES  CL_STATUS  \\\n",
       "0      40      3     120      80     69    148      185    3          8   \n",
       "1      54      1     141     100     65    171      363    3          7   \n",
       "2      52      4     145      90     66    183      289    3          7   \n",
       "3      34      2     130      90     61    120      317    2          5   \n",
       "4      51      3     138     100     72    208      296    3          7   \n",
       "5      43      1     120      85     70    134      220    3          8   \n",
       "6      25      1     110      80     74    190      235    3          8   \n",
       "7      61      1     150      90     69    142      247    3          6   \n",
       "8      36      3     135      80     69    155      290    3          8   \n",
       "9      49      2     110      75     70    141      150    3          8   \n",
       "\n",
       "   MD_62  SBP_62  DBP_62  CHOL_62  WT_62  IHD_DX  DEATH  Label   Score  \n",
       "0      3     110      64      241    152       2      1      0  0.7080  \n",
       "1      2     180     100      276    154       2      1      1  0.7552  \n",
       "2      3     150      88      299    143       6      0      1  0.5005  \n",
       "3      2     122      68      248    118       6      0      0  0.7111  \n",
       "4      1     135      95      286    199       8      0      0  0.5276  \n",
       "5      4     140      85      354    141       0      1      0  0.7044  \n",
       "6      2     116      90      280    210       0      1      0  0.7165  \n",
       "7      2     150      90      218    174       0      1      1  0.7498  \n",
       "8      2     120      84      238    157       0      0      0  0.7111  \n",
       "9      4     170      98      221    147       0      0      0  0.5451  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_model(final_best, data = data_unseen)"
   ]
  },
  {
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   "id": "ce66804a",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
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