EndtoEnd code for Predictive model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_excel(\"bank.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>oct</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>may</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital  education default  balance housing loan  \\\n",
       "0   30   unemployed  married    primary      no     1787      no   no   \n",
       "1   33     services  married  secondary      no     4789     yes  yes   \n",
       "2   35   management   single   tertiary      no     1350     yes   no   \n",
       "3   30   management  married   tertiary      no     1476     yes  yes   \n",
       "4   59  blue-collar  married  secondary      no        0     yes   no   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  cellular   19   oct        79         1     -1         0  unknown  no  \n",
       "1  cellular   11   may       220         1    339         4  failure  no  \n",
       "2  cellular   16   apr       185         1    330         1  failure  no  \n",
       "3   unknown    3   jun       199         4     -1         0  unknown  no  \n",
       "4   unknown    5   may       226         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4521 entries, 0 to 4520\n",
      "Data columns (total 17 columns):\n",
      "age          4521 non-null int64\n",
      "job          4521 non-null object\n",
      "marital      4521 non-null object\n",
      "education    4521 non-null object\n",
      "default      4521 non-null object\n",
      "balance      4521 non-null int64\n",
      "housing      4521 non-null object\n",
      "loan         4521 non-null object\n",
      "contact      4521 non-null object\n",
      "day          4521 non-null int64\n",
      "month        4521 non-null object\n",
      "duration     4521 non-null int64\n",
      "campaign     4521 non-null int64\n",
      "pdays        4521 non-null int64\n",
      "previous     4521 non-null int64\n",
      "poutcome     4521 non-null object\n",
      "y            4521 non-null object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 600.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Convert Target to 1/0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['target'] = df['y'].apply(lambda x: 1 if x == 'yes' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4000\n",
       "1     521\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop('y',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Descriptive Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.88476\n",
       "1    0.11524\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "      <td>4521.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.170095</td>\n",
       "      <td>1422.657819</td>\n",
       "      <td>15.915284</td>\n",
       "      <td>263.961292</td>\n",
       "      <td>2.793630</td>\n",
       "      <td>39.766645</td>\n",
       "      <td>0.542579</td>\n",
       "      <td>0.115240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.576211</td>\n",
       "      <td>3009.638142</td>\n",
       "      <td>8.247667</td>\n",
       "      <td>259.856633</td>\n",
       "      <td>3.109807</td>\n",
       "      <td>100.121124</td>\n",
       "      <td>1.693562</td>\n",
       "      <td>0.319347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>-3313.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>444.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>1480.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>71188.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3025.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       balance          day     duration     campaign  \\\n",
       "count  4521.000000   4521.000000  4521.000000  4521.000000  4521.000000   \n",
       "mean     41.170095   1422.657819    15.915284   263.961292     2.793630   \n",
       "std      10.576211   3009.638142     8.247667   259.856633     3.109807   \n",
       "min      19.000000  -3313.000000     1.000000     4.000000     1.000000   \n",
       "25%      33.000000     69.000000     9.000000   104.000000     1.000000   \n",
       "50%      39.000000    444.000000    16.000000   185.000000     2.000000   \n",
       "75%      49.000000   1480.000000    21.000000   329.000000     3.000000   \n",
       "max      87.000000  71188.000000    31.000000  3025.000000    50.000000   \n",
       "\n",
       "             pdays     previous       target  \n",
       "count  4521.000000  4521.000000  4521.000000  \n",
       "mean     39.766645     0.542579     0.115240  \n",
       "std     100.121124     1.693562     0.319347  \n",
       "min      -1.000000     0.000000     0.000000  \n",
       "25%      -1.000000     0.000000     0.000000  \n",
       "50%      -1.000000     0.000000     0.000000  \n",
       "75%      -1.000000     0.000000     0.000000  \n",
       "max     871.000000    25.000000     1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "job          object\n",
       "marital      object\n",
       "education    object\n",
       "default      object\n",
       "balance       int64\n",
       "housing      object\n",
       "loan         object\n",
       "contact      object\n",
       "day           int64\n",
       "month        object\n",
       "duration      int64\n",
       "campaign      int64\n",
       "pdays         int64\n",
       "previous      int64\n",
       "poutcome     object\n",
       "target        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c307390>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IeCAiXgQuAg7u5rgvA98Anu/thE4IZmaZdPZha8LGwJK6x0tT2Usk7QxsGhG/b+aEbZEQ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\nR8mIY44yfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr, \n",
    "        xticklabels=corr.columns,\n",
    "        yticklabels=corr.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Variable Selection using Python - Vote based approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data transformation\n",
    "# Convert categorical values to numeric using label encoder\n",
    "from sklearn import preprocessing\n",
    "from collections import defaultdict\n",
    "d = defaultdict(preprocessing.LabelEncoder)\n",
    "\n",
    "# Encoding the categorical variable\n",
    "fit = df.select_dtypes(include=['object']).fillna('NA').apply(lambda x: d[x.name].fit_transform(x))\n",
    "\n",
    "#Convert the categorical columns based on encoding\n",
    "for i in list(d.keys()):\n",
    "    df[i] = d[i].transform(df[i].fillna('NA'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = df[df.columns.difference(['target'])]\n",
    "labels = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = features.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 WOE and IV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas.core.algorithms as algos\n",
    "from pandas import Series\n",
    "import scipy.stats.stats as stats\n",
    "import re\n",
    "import traceback\n",
    "import string\n",
    "\n",
    "max_bin = 20\n",
    "force_bin = 3\n",
    "\n",
    "# define a binning function\n",
    "def mono_bin(Y, X, n = max_bin):\n",
    "    \n",
    "    df1 = pd.DataFrame({\"X\": X, \"Y\": Y})\n",
    "    justmiss = df1[['X','Y']][df1.X.isnull()]\n",
    "    notmiss = df1[['X','Y']][df1.X.notnull()]\n",
    "    r = 0\n",
    "    while np.abs(r) < 1:\n",
    "        try:\n",
    "            d1 = pd.DataFrame({\"X\": notmiss.X, \"Y\": notmiss.Y, \"Bucket\": pd.qcut(notmiss.X, n)})\n",
    "            d2 = d1.groupby('Bucket', as_index=True)\n",
    "            r, p = stats.spearmanr(d2.mean().X, d2.mean().Y)\n",
    "            n = n - 1 \n",
    "        except Exception as e:\n",
    "            n = n - 1\n",
    "\n",
    "    if len(d2) == 1:\n",
    "        n = force_bin         \n",
    "        bins = algos.quantile(notmiss.X, np.linspace(0, 1, n))\n",
    "        if len(np.unique(bins)) == 2:\n",
    "            bins = np.insert(bins, 0, 1)\n",
    "            bins[1] = bins[1]-(bins[1]/2)\n",
    "        d1 = pd.DataFrame({\"X\": notmiss.X, \"Y\": notmiss.Y, \"Bucket\": pd.cut(notmiss.X, np.unique(bins),include_lowest=True)}) \n",
    "        d2 = d1.groupby('Bucket', as_index=True)\n",
    "    \n",
    "    d3 = pd.DataFrame({},index=[])\n",
    "    d3[\"MIN_VALUE\"] = d2.min().X\n",
    "    d3[\"MAX_VALUE\"] = d2.max().X\n",
    "    d3[\"COUNT\"] = d2.count().Y\n",
    "    d3[\"EVENT\"] = d2.sum().Y\n",
    "    d3[\"NONEVENT\"] = d2.count().Y - d2.sum().Y\n",
    "    d3=d3.reset_index(drop=True)\n",
    "    \n",
    "    if len(justmiss.index) > 0:\n",
    "        d4 = pd.DataFrame({'MIN_VALUE':np.nan},index=[0])\n",
    "        d4[\"MAX_VALUE\"] = np.nan\n",
    "        d4[\"COUNT\"] = justmiss.count().Y\n",
    "        d4[\"EVENT\"] = justmiss.sum().Y\n",
    "        d4[\"NONEVENT\"] = justmiss.count().Y - justmiss.sum().Y\n",
    "        d3 = d3.append(d4,ignore_index=True)\n",
    "    \n",
    "    d3[\"EVENT_RATE\"] = d3.EVENT/d3.COUNT\n",
    "    d3[\"NON_EVENT_RATE\"] = d3.NONEVENT/d3.COUNT\n",
    "    d3[\"DIST_EVENT\"] = d3.EVENT/d3.sum().EVENT\n",
    "    d3[\"DIST_NON_EVENT\"] = d3.NONEVENT/d3.sum().NONEVENT\n",
    "    d3[\"WOE\"] = np.log(d3.DIST_EVENT/d3.DIST_NON_EVENT)\n",
    "    d3[\"IV\"] = (d3.DIST_EVENT-d3.DIST_NON_EVENT)*np.log(d3.DIST_EVENT/d3.DIST_NON_EVENT)\n",
    "    d3[\"VAR_NAME\"] = \"VAR\"\n",
    "    d3 = d3[['VAR_NAME','MIN_VALUE', 'MAX_VALUE', 'COUNT', 'EVENT', 'EVENT_RATE', 'NONEVENT', 'NON_EVENT_RATE', 'DIST_EVENT','DIST_NON_EVENT','WOE', 'IV']]       \n",
    "    d3 = d3.replace([np.inf, -np.inf], 0)\n",
    "    d3.IV = d3.IV.sum()\n",
    "    \n",
    "    return(d3)\n",
    "\n",
    "def char_bin(Y, X):\n",
    "        \n",
    "    df1 = pd.DataFrame({\"X\": X, \"Y\": Y})\n",
    "    justmiss = df1[['X','Y']][df1.X.isnull()]\n",
    "    notmiss = df1[['X','Y']][df1.X.notnull()]    \n",
    "    df2 = notmiss.groupby('X',as_index=True)\n",
    "    \n",
    "    d3 = pd.DataFrame({},index=[])\n",
    "    d3[\"COUNT\"] = df2.count().Y\n",
    "    d3[\"MIN_VALUE\"] = df2.sum().Y.index\n",
    "    d3[\"MAX_VALUE\"] = d3[\"MIN_VALUE\"]\n",
    "    d3[\"EVENT\"] = df2.sum().Y\n",
    "    d3[\"NONEVENT\"] = df2.count().Y - df2.sum().Y\n",
    "    \n",
    "    if len(justmiss.index) > 0:\n",
    "        d4 = pd.DataFrame({'MIN_VALUE':np.nan},index=[0])\n",
    "        d4[\"MAX_VALUE\"] = np.nan\n",
    "        d4[\"COUNT\"] = justmiss.count().Y\n",
    "        d4[\"EVENT\"] = justmiss.sum().Y\n",
    "        d4[\"NONEVENT\"] = justmiss.count().Y - justmiss.sum().Y\n",
    "        d3 = d3.append(d4,ignore_index=True)\n",
    "    \n",
    "    d3[\"EVENT_RATE\"] = d3.EVENT/d3.COUNT\n",
    "    d3[\"NON_EVENT_RATE\"] = d3.NONEVENT/d3.COUNT\n",
    "    d3[\"DIST_EVENT\"] = d3.EVENT/d3.sum().EVENT\n",
    "    d3[\"DIST_NON_EVENT\"] = d3.NONEVENT/d3.sum().NONEVENT\n",
    "    d3[\"WOE\"] = np.log(d3.DIST_EVENT/d3.DIST_NON_EVENT)\n",
    "    d3[\"IV\"] = (d3.DIST_EVENT-d3.DIST_NON_EVENT)*np.log(d3.DIST_EVENT/d3.DIST_NON_EVENT)\n",
    "    d3[\"VAR_NAME\"] = \"VAR\"\n",
    "    d3 = d3[['VAR_NAME','MIN_VALUE', 'MAX_VALUE', 'COUNT', 'EVENT', 'EVENT_RATE', 'NONEVENT', 'NON_EVENT_RATE', 'DIST_EVENT','DIST_NON_EVENT','WOE', 'IV']]      \n",
    "    d3 = d3.replace([np.inf, -np.inf], 0)\n",
    "    d3.IV = d3.IV.sum()\n",
    "    d3 = d3.reset_index(drop=True)\n",
    "    \n",
    "    return(d3)\n",
    "\n",
    "def data_vars(df1, target):\n",
    "    \n",
    "    stack = traceback.extract_stack()\n",
    "    filename, lineno, function_name, code = stack[-2]\n",
    "    vars_name = re.compile(r'\\((.*?)\\).*$').search(code).groups()[0]\n",
    "    final = (re.findall(r\"[\\w']+\", vars_name))[-1]\n",
    "    \n",
    "    x = df1.dtypes.index\n",
    "    count = -1\n",
    "    \n",
    "    for i in x:\n",
    "        if i.upper() not in (final.upper()):\n",
    "            if np.issubdtype(df1[i], np.number) and len(Series.unique(df1[i])) > 2:\n",
    "                conv = mono_bin(target, df1[i])\n",
    "                conv[\"VAR_NAME\"] = i\n",
    "                count = count + 1\n",
    "            else:\n",
    "                conv = char_bin(target, df1[i])\n",
    "                conv[\"VAR_NAME\"] = i            \n",
    "                count = count + 1\n",
    "                \n",
    "            if count == 0:\n",
    "                iv_df = conv\n",
    "            else:\n",
    "                iv_df = iv_df.append(conv,ignore_index=True)\n",
    "    \n",
    "    iv = pd.DataFrame({'IV':iv_df.groupby('VAR_NAME').IV.max()})\n",
    "    iv = iv.reset_index()\n",
    "    return(iv_df,iv) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_iv, IV = data_vars(df[df.columns.difference(['target'])],df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VAR_NAME</th>\n",
       "      <th>MIN_VALUE</th>\n",
       "      <th>MAX_VALUE</th>\n",
       "      <th>COUNT</th>\n",
       "      <th>EVENT</th>\n",
       "      <th>EVENT_RATE</th>\n",
       "      <th>NONEVENT</th>\n",
       "      <th>NON_EVENT_RATE</th>\n",
       "      <th>DIST_EVENT</th>\n",
       "      <th>DIST_NON_EVENT</th>\n",
       "      <th>WOE</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>19</td>\n",
       "      <td>39</td>\n",
       "      <td>2290</td>\n",
       "      <td>259</td>\n",
       "      <td>0.113100</td>\n",
       "      <td>2031</td>\n",
       "      <td>0.886900</td>\n",
       "      <td>0.497121</td>\n",
       "      <td>0.50775</td>\n",
       "      <td>-0.021156</td>\n",
       "      <td>0.000452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>40</td>\n",
       "      <td>87</td>\n",
       "      <td>2231</td>\n",
       "      <td>262</td>\n",
       "      <td>0.117436</td>\n",
       "      <td>1969</td>\n",
       "      <td>0.882564</td>\n",
       "      <td>0.502879</td>\n",
       "      <td>0.49225</td>\n",
       "      <td>0.021363</td>\n",
       "      <td>0.000452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>balance</td>\n",
       "      <td>-3313</td>\n",
       "      <td>69</td>\n",
       "      <td>1133</td>\n",
       "      <td>94</td>\n",
       "      <td>0.082966</td>\n",
       "      <td>1039</td>\n",
       "      <td>0.917034</td>\n",
       "      <td>0.180422</td>\n",
       "      <td>0.25975</td>\n",
       "      <td>-0.364420</td>\n",
       "      <td>0.076208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>balance</td>\n",
       "      <td>70</td>\n",
       "      <td>444</td>\n",
       "      <td>1128</td>\n",
       "      <td>106</td>\n",
       "      <td>0.093972</td>\n",
       "      <td>1022</td>\n",
       "      <td>0.906028</td>\n",
       "      <td>0.203455</td>\n",
       "      <td>0.25550</td>\n",
       "      <td>-0.227778</td>\n",
       "      <td>0.076208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>balance</td>\n",
       "      <td>445</td>\n",
       "      <td>1480</td>\n",
       "      <td>1131</td>\n",
       "      <td>149</td>\n",
       "      <td>0.131742</td>\n",
       "      <td>982</td>\n",
       "      <td>0.868258</td>\n",
       "      <td>0.285988</td>\n",
       "      <td>0.24550</td>\n",
       "      <td>0.152655</td>\n",
       "      <td>0.076208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>balance</td>\n",
       "      <td>1482</td>\n",
       "      <td>71188</td>\n",
       "      <td>1129</td>\n",
       "      <td>172</td>\n",
       "      <td>0.152347</td>\n",
       "      <td>957</td>\n",
       "      <td>0.847653</td>\n",
       "      <td>0.330134</td>\n",
       "      <td>0.23925</td>\n",
       "      <td>0.321991</td>\n",
       "      <td>0.076208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>campaign</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2998</td>\n",
       "      <td>378</td>\n",
       "      <td>0.126084</td>\n",
       "      <td>2620</td>\n",
       "      <td>0.873916</td>\n",
       "      <td>0.725528</td>\n",
       "      <td>0.65500</td>\n",
       "      <td>0.102264</td>\n",
       "      <td>0.023342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>campaign</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>1523</td>\n",
       "      <td>143</td>\n",
       "      <td>0.093894</td>\n",
       "      <td>1380</td>\n",
       "      <td>0.906106</td>\n",
       "      <td>0.274472</td>\n",
       "      <td>0.34500</td>\n",
       "      <td>-0.228695</td>\n",
       "      <td>0.023342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>contact</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3197</td>\n",
       "      <td>460</td>\n",
       "      <td>0.143885</td>\n",
       "      <td>2737</td>\n",
       "      <td>0.856115</td>\n",
       "      <td>0.882917</td>\n",
       "      <td>0.68425</td>\n",
       "      <td>0.254908</td>\n",
       "      <td>0.247734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>contact</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1324</td>\n",
       "      <td>61</td>\n",
       "      <td>0.046073</td>\n",
       "      <td>1263</td>\n",
       "      <td>0.953927</td>\n",
       "      <td>0.117083</td>\n",
       "      <td>0.31575</td>\n",
       "      <td>-0.992072</td>\n",
       "      <td>0.247734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>day</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2338</td>\n",
       "      <td>285</td>\n",
       "      <td>0.121899</td>\n",
       "      <td>2053</td>\n",
       "      <td>0.878101</td>\n",
       "      <td>0.547025</td>\n",
       "      <td>0.51325</td>\n",
       "      <td>0.063731</td>\n",
       "      <td>0.004581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>day</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>2183</td>\n",
       "      <td>236</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>1947</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.452975</td>\n",
       "      <td>0.48675</td>\n",
       "      <td>-0.071914</td>\n",
       "      <td>0.004581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>default</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4445</td>\n",
       "      <td>512</td>\n",
       "      <td>0.115186</td>\n",
       "      <td>3933</td>\n",
       "      <td>0.884814</td>\n",
       "      <td>0.982726</td>\n",
       "      <td>0.98325</td>\n",
       "      <td>-0.000534</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>default</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>0.118421</td>\n",
       "      <td>67</td>\n",
       "      <td>0.881579</td>\n",
       "      <td>0.017274</td>\n",
       "      <td>0.01675</td>\n",
       "      <td>0.030832</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>duration</td>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>510</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001961</td>\n",
       "      <td>509</td>\n",
       "      <td>0.998039</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>0.12725</td>\n",
       "      <td>-4.194148</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>duration</td>\n",
       "      <td>63</td>\n",
       "      <td>96</td>\n",
       "      <td>502</td>\n",
       "      <td>10</td>\n",
       "      <td>0.019920</td>\n",
       "      <td>492</td>\n",
       "      <td>0.980080</td>\n",
       "      <td>0.019194</td>\n",
       "      <td>0.12300</td>\n",
       "      <td>-1.857594</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>duration</td>\n",
       "      <td>97</td>\n",
       "      <td>128</td>\n",
       "      <td>509</td>\n",
       "      <td>16</td>\n",
       "      <td>0.031434</td>\n",
       "      <td>493</td>\n",
       "      <td>0.968566</td>\n",
       "      <td>0.030710</td>\n",
       "      <td>0.12325</td>\n",
       "      <td>-1.389621</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>duration</td>\n",
       "      <td>129</td>\n",
       "      <td>163</td>\n",
       "      <td>490</td>\n",
       "      <td>20</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>470</td>\n",
       "      <td>0.959184</td>\n",
       "      <td>0.038388</td>\n",
       "      <td>0.11750</td>\n",
       "      <td>-1.118701</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>duration</td>\n",
       "      <td>164</td>\n",
       "      <td>208</td>\n",
       "      <td>505</td>\n",
       "      <td>26</td>\n",
       "      <td>0.051485</td>\n",
       "      <td>479</td>\n",
       "      <td>0.948515</td>\n",
       "      <td>0.049904</td>\n",
       "      <td>0.11975</td>\n",
       "      <td>-0.875304</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>duration</td>\n",
       "      <td>209</td>\n",
       "      <td>261</td>\n",
       "      <td>498</td>\n",
       "      <td>61</td>\n",
       "      <td>0.122490</td>\n",
       "      <td>437</td>\n",
       "      <td>0.877510</td>\n",
       "      <td>0.117083</td>\n",
       "      <td>0.10925</td>\n",
       "      <td>0.069240</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>duration</td>\n",
       "      <td>262</td>\n",
       "      <td>354</td>\n",
       "      <td>505</td>\n",
       "      <td>73</td>\n",
       "      <td>0.144554</td>\n",
       "      <td>432</td>\n",
       "      <td>0.855446</td>\n",
       "      <td>0.140115</td>\n",
       "      <td>0.10800</td>\n",
       "      <td>0.260333</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>duration</td>\n",
       "      <td>355</td>\n",
       "      <td>546</td>\n",
       "      <td>500</td>\n",
       "      <td>101</td>\n",
       "      <td>0.202000</td>\n",
       "      <td>399</td>\n",
       "      <td>0.798000</td>\n",
       "      <td>0.193858</td>\n",
       "      <td>0.09975</td>\n",
       "      <td>0.664459</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>duration</td>\n",
       "      <td>547</td>\n",
       "      <td>3025</td>\n",
       "      <td>502</td>\n",
       "      <td>213</td>\n",
       "      <td>0.424303</td>\n",
       "      <td>289</td>\n",
       "      <td>0.575697</td>\n",
       "      <td>0.408829</td>\n",
       "      <td>0.07225</td>\n",
       "      <td>1.733165</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>education</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2984</td>\n",
       "      <td>309</td>\n",
       "      <td>0.103552</td>\n",
       "      <td>2675</td>\n",
       "      <td>0.896448</td>\n",
       "      <td>0.593090</td>\n",
       "      <td>0.66875</td>\n",
       "      <td>-0.120064</td>\n",
       "      <td>0.024649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>education</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1537</td>\n",
       "      <td>212</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>1325</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>0.406910</td>\n",
       "      <td>0.33125</td>\n",
       "      <td>0.205718</td>\n",
       "      <td>0.024649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>housing</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1962</td>\n",
       "      <td>301</td>\n",
       "      <td>0.153415</td>\n",
       "      <td>1661</td>\n",
       "      <td>0.846585</td>\n",
       "      <td>0.577735</td>\n",
       "      <td>0.41525</td>\n",
       "      <td>0.330235</td>\n",
       "      <td>0.106556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>housing</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2559</td>\n",
       "      <td>220</td>\n",
       "      <td>0.085971</td>\n",
       "      <td>2339</td>\n",
       "      <td>0.914029</td>\n",
       "      <td>0.422265</td>\n",
       "      <td>0.58475</td>\n",
       "      <td>-0.325552</td>\n",
       "      <td>0.106556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>job</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2673</td>\n",
       "      <td>287</td>\n",
       "      <td>0.107370</td>\n",
       "      <td>2386</td>\n",
       "      <td>0.892630</td>\n",
       "      <td>0.550864</td>\n",
       "      <td>0.59650</td>\n",
       "      <td>-0.079592</td>\n",
       "      <td>0.008522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>job</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>1848</td>\n",
       "      <td>234</td>\n",
       "      <td>0.126623</td>\n",
       "      <td>1614</td>\n",
       "      <td>0.873377</td>\n",
       "      <td>0.449136</td>\n",
       "      <td>0.40350</td>\n",
       "      <td>0.107150</td>\n",
       "      <td>0.008522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>loan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3830</td>\n",
       "      <td>478</td>\n",
       "      <td>0.124804</td>\n",
       "      <td>3352</td>\n",
       "      <td>0.875196</td>\n",
       "      <td>0.917466</td>\n",
       "      <td>0.83800</td>\n",
       "      <td>0.090598</td>\n",
       "      <td>0.060791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>loan</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>691</td>\n",
       "      <td>43</td>\n",
       "      <td>0.062229</td>\n",
       "      <td>648</td>\n",
       "      <td>0.937771</td>\n",
       "      <td>0.082534</td>\n",
       "      <td>0.16200</td>\n",
       "      <td>-0.674391</td>\n",
       "      <td>0.060791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>marital</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3325</td>\n",
       "      <td>354</td>\n",
       "      <td>0.106466</td>\n",
       "      <td>2971</td>\n",
       "      <td>0.893534</td>\n",
       "      <td>0.679463</td>\n",
       "      <td>0.74275</td>\n",
       "      <td>-0.089057</td>\n",
       "      <td>0.019556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>marital</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1196</td>\n",
       "      <td>167</td>\n",
       "      <td>0.139632</td>\n",
       "      <td>1029</td>\n",
       "      <td>0.860368</td>\n",
       "      <td>0.320537</td>\n",
       "      <td>0.25725</td>\n",
       "      <td>0.219951</td>\n",
       "      <td>0.019556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>month</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2553</td>\n",
       "      <td>314</td>\n",
       "      <td>0.122993</td>\n",
       "      <td>2239</td>\n",
       "      <td>0.877007</td>\n",
       "      <td>0.602687</td>\n",
       "      <td>0.55975</td>\n",
       "      <td>0.073908</td>\n",
       "      <td>0.007580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>month</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>1968</td>\n",
       "      <td>207</td>\n",
       "      <td>0.105183</td>\n",
       "      <td>1761</td>\n",
       "      <td>0.894817</td>\n",
       "      <td>0.397313</td>\n",
       "      <td>0.44025</td>\n",
       "      <td>-0.102619</td>\n",
       "      <td>0.007580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>pdays</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>3705</td>\n",
       "      <td>337</td>\n",
       "      <td>0.090958</td>\n",
       "      <td>3368</td>\n",
       "      <td>0.909042</td>\n",
       "      <td>0.646833</td>\n",
       "      <td>0.84200</td>\n",
       "      <td>-0.263692</td>\n",
       "      <td>0.203267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>pdays</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003839</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.203267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>pdays</td>\n",
       "      <td>2</td>\n",
       "      <td>871</td>\n",
       "      <td>814</td>\n",
       "      <td>182</td>\n",
       "      <td>0.223587</td>\n",
       "      <td>632</td>\n",
       "      <td>0.776413</td>\n",
       "      <td>0.349328</td>\n",
       "      <td>0.15800</td>\n",
       "      <td>0.793417</td>\n",
       "      <td>0.203267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>687</td>\n",
       "      <td>101</td>\n",
       "      <td>0.147016</td>\n",
       "      <td>586</td>\n",
       "      <td>0.852984</td>\n",
       "      <td>0.193858</td>\n",
       "      <td>0.14650</td>\n",
       "      <td>0.280100</td>\n",
       "      <td>0.015968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3834</td>\n",
       "      <td>420</td>\n",
       "      <td>0.109546</td>\n",
       "      <td>3414</td>\n",
       "      <td>0.890454</td>\n",
       "      <td>0.806142</td>\n",
       "      <td>0.85350</td>\n",
       "      <td>-0.057086</td>\n",
       "      <td>0.015968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>previous</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3991</td>\n",
       "      <td>388</td>\n",
       "      <td>0.097219</td>\n",
       "      <td>3603</td>\n",
       "      <td>0.902781</td>\n",
       "      <td>0.744722</td>\n",
       "      <td>0.90075</td>\n",
       "      <td>-0.190217</td>\n",
       "      <td>0.177081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>previous</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>530</td>\n",
       "      <td>133</td>\n",
       "      <td>0.250943</td>\n",
       "      <td>397</td>\n",
       "      <td>0.749057</td>\n",
       "      <td>0.255278</td>\n",
       "      <td>0.09925</td>\n",
       "      <td>0.944712</td>\n",
       "      <td>0.177081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     VAR_NAME  MIN_VALUE  MAX_VALUE  COUNT  EVENT  EVENT_RATE  NONEVENT  \\\n",
       "0         age         19         39   2290    259    0.113100      2031   \n",
       "1         age         40         87   2231    262    0.117436      1969   \n",
       "2     balance      -3313         69   1133     94    0.082966      1039   \n",
       "3     balance         70        444   1128    106    0.093972      1022   \n",
       "4     balance        445       1480   1131    149    0.131742       982   \n",
       "5     balance       1482      71188   1129    172    0.152347       957   \n",
       "6    campaign          1          2   2998    378    0.126084      2620   \n",
       "7    campaign          3         50   1523    143    0.093894      1380   \n",
       "8     contact          0          1   3197    460    0.143885      2737   \n",
       "9     contact          2          2   1324     61    0.046073      1263   \n",
       "10        day          1         16   2338    285    0.121899      2053   \n",
       "11        day         17         31   2183    236    0.108108      1947   \n",
       "12    default          0          0   4445    512    0.115186      3933   \n",
       "13    default          1          1     76      9    0.118421        67   \n",
       "14   duration          4         62    510      1    0.001961       509   \n",
       "15   duration         63         96    502     10    0.019920       492   \n",
       "16   duration         97        128    509     16    0.031434       493   \n",
       "17   duration        129        163    490     20    0.040816       470   \n",
       "18   duration        164        208    505     26    0.051485       479   \n",
       "19   duration        209        261    498     61    0.122490       437   \n",
       "20   duration        262        354    505     73    0.144554       432   \n",
       "21   duration        355        546    500    101    0.202000       399   \n",
       "22   duration        547       3025    502    213    0.424303       289   \n",
       "23  education          0          1   2984    309    0.103552      2675   \n",
       "24  education          2          3   1537    212    0.137931      1325   \n",
       "25    housing          0          0   1962    301    0.153415      1661   \n",
       "26    housing          1          1   2559    220    0.085971      2339   \n",
       "27        job          0          4   2673    287    0.107370      2386   \n",
       "28        job          5         11   1848    234    0.126623      1614   \n",
       "29       loan          0          0   3830    478    0.124804      3352   \n",
       "30       loan          1          1    691     43    0.062229       648   \n",
       "31    marital          0          1   3325    354    0.106466      2971   \n",
       "32    marital          2          2   1196    167    0.139632      1029   \n",
       "33      month          0          6   2553    314    0.122993      2239   \n",
       "34      month          7         11   1968    207    0.105183      1761   \n",
       "35      pdays         -1         -1   3705    337    0.090958      3368   \n",
       "36      pdays          1          1      2      2    1.000000         0   \n",
       "37      pdays          2        871    814    182    0.223587       632   \n",
       "38   poutcome          0          1    687    101    0.147016       586   \n",
       "39   poutcome          2          3   3834    420    0.109546      3414   \n",
       "40   previous          0          1   3991    388    0.097219      3603   \n",
       "41   previous          2         25    530    133    0.250943       397   \n",
       "\n",
       "    NON_EVENT_RATE  DIST_EVENT  DIST_NON_EVENT       WOE        IV  \n",
       "0         0.886900    0.497121         0.50775 -0.021156  0.000452  \n",
       "1         0.882564    0.502879         0.49225  0.021363  0.000452  \n",
       "2         0.917034    0.180422         0.25975 -0.364420  0.076208  \n",
       "3         0.906028    0.203455         0.25550 -0.227778  0.076208  \n",
       "4         0.868258    0.285988         0.24550  0.152655  0.076208  \n",
       "5         0.847653    0.330134         0.23925  0.321991  0.076208  \n",
       "6         0.873916    0.725528         0.65500  0.102264  0.023342  \n",
       "7         0.906106    0.274472         0.34500 -0.228695  0.023342  \n",
       "8         0.856115    0.882917         0.68425  0.254908  0.247734  \n",
       "9         0.953927    0.117083         0.31575 -0.992072  0.247734  \n",
       "10        0.878101    0.547025         0.51325  0.063731  0.004581  \n",
       "11        0.891892    0.452975         0.48675 -0.071914  0.004581  \n",
       "12        0.884814    0.982726         0.98325 -0.000534  0.000016  \n",
       "13        0.881579    0.017274         0.01675  0.030832  0.000016  \n",
       "14        0.998039    0.001919         0.12725 -4.194148  1.651501  \n",
       "15        0.980080    0.019194         0.12300 -1.857594  1.651501  \n",
       "16        0.968566    0.030710         0.12325 -1.389621  1.651501  \n",
       "17        0.959184    0.038388         0.11750 -1.118701  1.651501  \n",
       "18        0.948515    0.049904         0.11975 -0.875304  1.651501  \n",
       "19        0.877510    0.117083         0.10925  0.069240  1.651501  \n",
       "20        0.855446    0.140115         0.10800  0.260333  1.651501  \n",
       "21        0.798000    0.193858         0.09975  0.664459  1.651501  \n",
       "22        0.575697    0.408829         0.07225  1.733165  1.651501  \n",
       "23        0.896448    0.593090         0.66875 -0.120064  0.024649  \n",
       "24        0.862069    0.406910         0.33125  0.205718  0.024649  \n",
       "25        0.846585    0.577735         0.41525  0.330235  0.106556  \n",
       "26        0.914029    0.422265         0.58475 -0.325552  0.106556  \n",
       "27        0.892630    0.550864         0.59650 -0.079592  0.008522  \n",
       "28        0.873377    0.449136         0.40350  0.107150  0.008522  \n",
       "29        0.875196    0.917466         0.83800  0.090598  0.060791  \n",
       "30        0.937771    0.082534         0.16200 -0.674391  0.060791  \n",
       "31        0.893534    0.679463         0.74275 -0.089057  0.019556  \n",
       "32        0.860368    0.320537         0.25725  0.219951  0.019556  \n",
       "33        0.877007    0.602687         0.55975  0.073908  0.007580  \n",
       "34        0.894817    0.397313         0.44025 -0.102619  0.007580  \n",
       "35        0.909042    0.646833         0.84200 -0.263692  0.203267  \n",
       "36        0.000000    0.003839         0.00000  0.000000  0.203267  \n",
       "37        0.776413    0.349328         0.15800  0.793417  0.203267  \n",
       "38        0.852984    0.193858         0.14650  0.280100  0.015968  \n",
       "39        0.890454    0.806142         0.85350 -0.057086  0.015968  \n",
       "40        0.902781    0.744722         0.90075 -0.190217  0.177081  \n",
       "41        0.749057    0.255278         0.09925  0.944712  0.177081  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>duration</td>\n",
       "      <td>1.651501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>contact</td>\n",
       "      <td>0.247734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pdays</td>\n",
       "      <td>0.203267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>previous</td>\n",
       "      <td>0.177081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>housing</td>\n",
       "      <td>0.106556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>balance</td>\n",
       "      <td>0.076208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>loan</td>\n",
       "      <td>0.060791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>education</td>\n",
       "      <td>0.024649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>0.023342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>marital</td>\n",
       "      <td>0.019556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>0.015968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>job</td>\n",
       "      <td>0.008522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>month</td>\n",
       "      <td>0.007580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>day</td>\n",
       "      <td>0.004581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.000452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>default</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index        IV\n",
       "6    duration  1.651501\n",
       "3     contact  0.247734\n",
       "13      pdays  0.203267\n",
       "15   previous  0.177081\n",
       "8     housing  0.106556\n",
       "1     balance  0.076208\n",
       "10       loan  0.060791\n",
       "7   education  0.024649\n",
       "2    campaign  0.023342\n",
       "11    marital  0.019556\n",
       "14   poutcome  0.015968\n",
       "9         job  0.008522\n",
       "12      month  0.007580\n",
       "4         day  0.004581\n",
       "0         age  0.000452\n",
       "5     default  0.000016"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IV = IV.rename(columns={'VAR_NAME':'index'})\n",
    "IV.sort_values(['IV'],ascending=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply WOE values to your dataframe columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below code snippet can be used to apply the WOE values to your dataframe columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transform_vars_list = df.columns.difference(['target'])\n",
    "transform_prefix = 'new_' # leave this value blank if you need replace the original column values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'balance', 'campaign', 'contact', 'day', 'default', 'duration',\n",
       "       'education', 'housing', 'job', 'loan', 'marital', 'month', 'pdays',\n",
       "       'poutcome', 'previous'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transform_vars_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for var in transform_vars_list:\n",
    "    small_df = final_iv[final_iv['VAR_NAME'] == var]\n",
    "    transform_dict = dict(zip(small_df.MAX_VALUE,small_df.WOE))\n",
    "    replace_cmd = ''\n",
    "    replace_cmd1 = ''\n",
    "    for i in sorted(transform_dict.items()):\n",
    "        replace_cmd = replace_cmd + str(i[1]) + str(' if x <= ') + str(i[0]) + ' else '\n",
    "        replace_cmd1 = replace_cmd1 + str(i[1]) + str(' if x == \"') + str(i[0]) + '\" else '\n",
    "    replace_cmd = replace_cmd + '0'\n",
    "    replace_cmd1 = replace_cmd1 + '0'\n",
    "    if replace_cmd != '0':\n",
    "        try:\n",
    "            df[transform_prefix + var] = df[var].apply(lambda x: eval(replace_cmd))\n",
    "        except:\n",
    "            df[transform_prefix + var] = df[var].apply(lambda x: eval(replace_cmd1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Variable Importance using Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9898252598982527\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mbagav200/anaconda/lib/python3.5/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "clf.fit(features,labels)\n",
    "\n",
    "preds = clf.predict(features)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(preds,labels)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "VI = DataFrame(clf.feature_importances_, columns = [\"RF\"], index=features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>RF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>duration</td>\n",
       "      <td>0.281990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>balance</td>\n",
       "      <td>0.108598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.105884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>day</td>\n",
       "      <td>0.098096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>month</td>\n",
       "      <td>0.084477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>job</td>\n",
       "      <td>0.049138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pdays</td>\n",
       "      <td>0.044520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>0.041653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>0.037790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>previous</td>\n",
       "      <td>0.029960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>education</td>\n",
       "      <td>0.029073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>marital</td>\n",
       "      <td>0.026787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>contact</td>\n",
       "      <td>0.025842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>housing</td>\n",
       "      <td>0.025114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>loan</td>\n",
       "      <td>0.009460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>default</td>\n",
       "      <td>0.001620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index        RF\n",
       "6    duration  0.281990\n",
       "1     balance  0.108598\n",
       "0         age  0.105884\n",
       "4         day  0.098096\n",
       "12      month  0.084477\n",
       "9         job  0.049138\n",
       "13      pdays  0.044520\n",
       "14   poutcome  0.041653\n",
       "2    campaign  0.037790\n",
       "15   previous  0.029960\n",
       "7   education  0.029073\n",
       "11    marital  0.026787\n",
       "3     contact  0.025842\n",
       "8     housing  0.025114\n",
       "10       loan  0.009460\n",
       "5     default  0.001620"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VI = VI.reset_index()\n",
    "VI.sort_values(['RF'],ascending=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()\n",
    "rfe = RFE(model, 20)\n",
    "fit = rfe.fit(features, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "Selected = DataFrame(rfe.support_, columns = [\"RFE\"], index=features.columns)\n",
    "Selected = Selected.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>index</th>\n",
       "      <th>RFE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>balance</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>contact</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>day</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>default</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>duration</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>education</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>housing</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>job</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>loan</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>marital</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>month</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pdays</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>previous</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index   RFE\n",
       "0         age  True\n",
       "1     balance  True\n",
       "2    campaign  True\n",
       "3     contact  True\n",
       "4         day  True\n",
       "5     default  True\n",
       "6    duration  True\n",
       "7   education  True\n",
       "8     housing  True\n",
       "9         job  True\n",
       "10       loan  True\n",
       "11    marital  True\n",
       "12      month  True\n",
       "13      pdays  True\n",
       "14   poutcome  True\n",
       "15   previous  True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Selected[Selected['RFE'] == True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 Variable Importance using Extratrees Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.09817079 0.08674032 0.05639184 0.02710927 0.08751913 0.00473037\n",
      " 0.24184737 0.04591393 0.02262146 0.06606975 0.01250787 0.03845824\n",
      " 0.08927791 0.04124209 0.05230093 0.02909875]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "model = ExtraTreesClassifier()\n",
    "model.fit(features, labels)\n",
    "\n",
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "FI = DataFrame(model.feature_importances_, columns = [\"Extratrees\"], index=features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "FI = FI.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Extratrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>duration</td>\n",
       "      <td>0.241847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0.098171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>month</td>\n",
       "      <td>0.089278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>day</td>\n",
       "      <td>0.087519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>balance</td>\n",
       "      <td>0.086740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>job</td>\n",
       "      <td>0.066070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>0.056392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>0.052301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>education</td>\n",
       "      <td>0.045914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pdays</td>\n",
       "      <td>0.041242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>marital</td>\n",
       "      <td>0.038458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>previous</td>\n",
       "      <td>0.029099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>contact</td>\n",
       "      <td>0.027109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>housing</td>\n",
       "      <td>0.022621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>loan</td>\n",
       "      <td>0.012508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>default</td>\n",
       "      <td>0.004730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  Extratrees\n",
       "6    duration    0.241847\n",
       "0         age    0.098171\n",
       "12      month    0.089278\n",
       "4         day    0.087519\n",
       "1     balance    0.086740\n",
       "9         job    0.066070\n",
       "2    campaign    0.056392\n",
       "14   poutcome    0.052301\n",
       "7   education    0.045914\n",
       "13      pdays    0.041242\n",
       "11    marital    0.038458\n",
       "15   previous    0.029099\n",
       "3     contact    0.027109\n",
       "8     housing    0.022621\n",
       "10       loan    0.012508\n",
       "5     default    0.004730"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FI.sort_values(['Extratrees'],ascending=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 Chi Square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "model = SelectKBest(score_func=chi2, k=5)\n",
    "fit = model.fit(features.abs(), labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "chi_sq = DataFrame(fit.scores_, columns = [\"Chi_Square\"], index=features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chi_sq = chi_sq.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Chi_Square</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>duration</td>\n",
       "      <td>186041.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pdays</td>\n",
       "      <td>11573.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>balance</td>\n",
       "      <td>7997.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>previous</td>\n",
       "      <td>325.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>contact</td>\n",
       "      <td>100.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>58.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>24.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>housing</td>\n",
       "      <td>21.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>loan</td>\n",
       "      <td>19.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>month</td>\n",
       "      <td>12.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>11.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>job</td>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>education</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>day</td>\n",
       "      <td>2.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>marital</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>default</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  Chi_Square\n",
       "6    duration   186041.90\n",
       "13      pdays    11573.63\n",
       "1     balance     7997.11\n",
       "15   previous      325.48\n",
       "3     contact      100.51\n",
       "2    campaign       58.50\n",
       "0         age       24.97\n",
       "8     housing       21.50\n",
       "10       loan       19.05\n",
       "12      month       12.33\n",
       "14   poutcome       11.87\n",
       "9         job        8.15\n",
       "7   education        3.80\n",
       "4         day        2.44\n",
       "11    marital        0.32\n",
       "5     default        0.01"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi_sq.sort_values('Chi_Square',ascending=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6 L1 feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "lsvc = LinearSVC(C=0.01, penalty=\"l1\", dual=False).fit(features, labels)\n",
    "model = SelectFromModel(lsvc,prefit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "l1 = DataFrame(model.get_support(), columns = [\"L1\"], index=features.columns)\n",
    "l1 = l1.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>L1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>contact</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>day</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>duration</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>housing</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>job</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>loan</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>marital</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>month</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pdays</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>previous</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index    L1\n",
       "0        age  True\n",
       "2   campaign  True\n",
       "3    contact  True\n",
       "4        day  True\n",
       "6   duration  True\n",
       "8    housing  True\n",
       "9        job  True\n",
       "10      loan  True\n",
       "11   marital  True\n",
       "12     month  True\n",
       "13     pdays  True\n",
       "14  poutcome  True\n",
       "15  previous  True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1[l1['L1'] == True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.7 Combine all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from functools import reduce\n",
    "dfs = [IV, VI, Selected, FI, chi_sq, l1]\n",
    "final_results = reduce(lambda left,right: pd.merge(left,right,on='index'), dfs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.8 Vote each variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = ['IV', 'RF', 'Extratrees', 'Chi_Square']\n",
    "\n",
    "score_table = pd.DataFrame({},[])\n",
    "score_table['index'] = final_results['index']\n",
    "\n",
    "for i in columns:\n",
    "    score_table[i] = final_results['index'].isin(list(final_results.nlargest(5,i)['index'])).astype(int)\n",
    "    \n",
    "score_table['RFE'] = final_results['RFE'].astype(int)\n",
    "score_table['L1'] = final_results['L1'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "score_table['final_score'] = score_table.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>IV</th>\n",
       "      <th>RF</th>\n",
       "      <th>Extratrees</th>\n",
       "      <th>Chi_Square</th>\n",
       "      <th>RFE</th>\n",
       "      <th>L1</th>\n",
       "      <th>final_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>duration</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>balance</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>contact</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>month</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pdays</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>previous</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>housing</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>job</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>loan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>marital</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>poutcome</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>default</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>education</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  IV  RF  Extratrees  Chi_Square  RFE  L1  final_score\n",
       "6    duration   1   1           1           1    1   1            6\n",
       "0         age   0   1           1           0    1   1            4\n",
       "1     balance   0   1           1           1    1   0            4\n",
       "3     contact   1   0           0           1    1   1            4\n",
       "4         day   0   1           1           0    1   1            4\n",
       "12      month   0   1           1           0    1   1            4\n",
       "13      pdays   1   0           0           1    1   1            4\n",
       "15   previous   1   0           0           1    1   1            4\n",
       "8     housing   1   0           0           0    1   1            3\n",
       "2    campaign   0   0           0           0    1   1            2\n",
       "9         job   0   0           0           0    1   1            2\n",
       "10       loan   0   0           0           0    1   1            2\n",
       "11    marital   0   0           0           0    1   1            2\n",
       "14   poutcome   0   0           0           0    1   1            2\n",
       "5     default   0   0           0           0    1   0            1\n",
       "7   education   0   0           0           0    1   0            1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_table.sort_values('final_score',ascending=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.9 Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calculate_vif(features):\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"Features\"] = features.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(features.values, i) for i in range(features.shape[1])]    \n",
    "    return(vif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = features[list(score_table[score_table['final_score'] >= 2]['index'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vif = calculate_vif(features)\n",
    "while vif['VIF'][vif['VIF'] > 10].any():\n",
    "    remove = vif.sort_values('VIF',ascending=0)['Features'][:1]\n",
    "    features.drop(remove,axis=1,inplace=True)\n",
    "    vif = calculate_vif(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'balance',\n",
       " 'campaign',\n",
       " 'contact',\n",
       " 'day',\n",
       " 'duration',\n",
       " 'housing',\n",
       " 'job',\n",
       " 'loan',\n",
       " 'marital',\n",
       " 'month',\n",
       " 'pdays',\n",
       " 'previous']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(vif['Features'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Stats and Visualization - For selected variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_vars = list(vif['Features']) + ['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = df[final_vars].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>campaign</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>housing</th>\n",
       "      <th>job</th>\n",
       "      <th>loan</th>\n",
       "      <th>marital</th>\n",
       "      <th>month</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>4521.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.17</td>\n",
       "      <td>1422.66</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.65</td>\n",
       "      <td>15.92</td>\n",
       "      <td>263.96</td>\n",
       "      <td>0.57</td>\n",
       "      <td>4.41</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.15</td>\n",
       "      <td>5.54</td>\n",
       "      <td>39.77</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.58</td>\n",
       "      <td>3009.64</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.90</td>\n",
       "      <td>8.25</td>\n",
       "      <td>259.86</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.60</td>\n",
       "      <td>3.00</td>\n",
       "      <td>100.12</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.00</td>\n",
       "      <td>-3313.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.00</td>\n",
       "      <td>69.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>104.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.00</td>\n",
       "      <td>444.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>185.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.00</td>\n",
       "      <td>1480.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>329.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>87.00</td>\n",
       "      <td>71188.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>3025.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>871.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  balance  campaign  contact     day  duration  housing     job  \\\n",
       "count 4521.00  4521.00   4521.00  4521.00 4521.00   4521.00  4521.00 4521.00   \n",
       "mean    41.17  1422.66      2.79     0.65   15.92    263.96     0.57    4.41   \n",
       "std     10.58  3009.64      3.11     0.90    8.25    259.86     0.50    3.26   \n",
       "min     19.00 -3313.00      1.00     0.00    1.00      4.00     0.00    0.00   \n",
       "25%     33.00    69.00      1.00     0.00    9.00    104.00     0.00    1.00   \n",
       "50%     39.00   444.00      2.00     0.00   16.00    185.00     1.00    4.00   \n",
       "75%     49.00  1480.00      3.00     2.00   21.00    329.00     1.00    7.00   \n",
       "max     87.00 71188.00     50.00     2.00   31.00   3025.00     1.00   11.00   \n",
       "\n",
       "         loan  marital   month   pdays  previous  target  \n",
       "count 4521.00  4521.00 4521.00 4521.00   4521.00 4521.00  \n",
       "mean     0.15     1.15    5.54   39.77      0.54    0.12  \n",
       "std      0.36     0.60    3.00  100.12      1.69    0.32  \n",
       "min      0.00     0.00    0.00   -1.00      0.00    0.00  \n",
       "25%      0.00     1.00    3.00   -1.00      0.00    0.00  \n",
       "50%      0.00     1.00    6.00   -1.00      0.00    0.00  \n",
       "75%      0.00     2.00    8.00   -1.00      0.00    0.00  \n",
       "max      1.00     2.00   11.00  871.00     25.00    1.00  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZmWVyWJiZWSaHhZmZZXJYmJlZJoeFmZllcliYmVkmh4WZmWVyWJiZWSaHhZmZ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\ny/R/eOgxoSBSRw0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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L8h1GVv0GlvHPZUvzHYaZdWDNuXR2T0kPkpxlDJL0UeBrEfGNbB0ljSd5DkYh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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CzMxS5TQsJI2V9IakhZLOb2B+saSpyfw5kgYl7cdIel7SK8m/n81lnWZm1rSc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\nvoAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/2n6GpBclvZr++6k86zQzs4blFhaSSoA7gLOAwcBYSYNrdbsEWB8RhwKTgW+n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\n+j+U+ZoyL6f/zAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9edVqZmat657nxiNiDjCnqO26gtc1wPktrPcN4Bt51mZmZm1X0hPcZmZWGhwW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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KT9/9ofPGmL1DMq8s1gLR95Z2cssqZQOHArPcX/4dgGkiMjKBYxPSqVMnCgoK2LRpU30P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\nlyULY4wxcf1/lVol4AzosEEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DwszMMjkszMwsk8PCzMwy5RoWksZKWixpiaTLG1nfQdL0dP08SYMarB8oaZOk\ny/Ks08zMmpZbWEgqA24FTgeGA5MkDW/Q7WJgfUQMAW4Evtlg/Q3AL/Oq0czMipPnmcVoYElELI2I\nrcA0YFyDPuOAe9LlB4HTJAlA0jnAMmBhjjWamVkR8gyLfsDKgtfVaVujfSKiDtgI9JLUBfgS8NWm\nDiBpiqQqSVWrV69utsLNzGxXpTrBfTVwY0RsaqpTREyNiMqIqOzTp0/LVGZmdgAqz3Hfq4ABBa/7\np22N9amWVA50A9YCJwDjJX0L6A5sl1QbEd/NsV4zM9uNPMNiPjBU0mCSUJgIfKxBn5nAhcAzwHjg\n8YgI4D07Oki6GtjkoDAzaz25hUVE1Em6FJgNlAF3RcRCSdcAVRExE7gTuFfSEmAdSaCYmVmJyfPM\ngoiYBcxq0HZVwXItMCFjH1fnUpyZmRWtVCe4zcyshDgszMwsk8PCzMwyOSzMzCyTw8LMzDI5LMzM\nLJPDwszMMjkszMwsk8PCzMwyOSzMzCyTw8LMzDI5LMzMLJPDwszMMjkszMwsk8PCzMwyOSzMzCyT\nw8LMzDI5LMzMLFOuYSFprKTFkpZIuryR9R0kTU/Xz5M0KG1/v6TnJb2U/v2+POs0M7Om5RYWksqA\nW4HTgeHAJEnDG3S7GFgfEUOAG4Fvpu1rgA9FxDHAhcC9edVpZmbZ8jyzGA0siYilEbEVmAaMa9Bn\nHHBPuvwgcJokRcTvIuIvaftCoJOkDjnWamZmTcgzLPoBKwteV6dtjfaJiDpgI9CrQZ+PAi9ExJaG\nB5A0RVKVpKrVq1c3W+FmZrarkp7gljSCZGjqXxtbHxFTI6IyIir79OnTssWZmR1A8gyLVcCAgtf9\n07ZG+0gqB7oBa9PX/YGHgH+OiFdyrNPMzDLkGRbzgaGSBktqD0wEZjboM5NkAhtgPPB4RISk7sAj\nwOUR8dscazQzsyLkFhbpHMSlwGxgETAjIhZKukbS2Wm3O4FekpYA/wbsuLz2UmAIcJWkBemfd+RV\nq5mZNa08z51HxCxgVoO2qwqWa4EJjWz3deDredZmZmbFK+kJbjMzKw0OCzMzy+SwMDOzTA4LMzPL\n5LAwM7NMDgszM8vksDAzs0wOCzMzy+SwMDOzTA4LMzPL5LAwM7NMDgszM8vksDAzs0wOCzMzy+Sw\nMDOzTA4LMzPL5LAwM7NMDgszM8uUa1hIGitpsaQlki5vZH0HSdPT9fMkDSpYd0XavljSB/Os08zM\nmpZbWEgqA24FTgeGA5MkDW/Q7WJgfUQMAW4EvpluOxyYCIwAxgK3pfszM7NWkOeZxWhgSUQsjYit\nwDRgXIM+44B70uUHgdMkKW3kG7bMAAADt0lEQVSfFhFbImIZsCTdn5mZtYLyHPfdD1hZ8LoaOGF3\nfSKiTtJGoFfa/myDbfs1PICkKcCU9OUmSYubp/R8bbvs3NYuoRi9gTWtXUQxkt8vrLn4+7P57Cff\nm+8splOeYZG7iJgKTG3tOtoiSVURUdnadZg1xt+fLS/PYahVwICC1/3Ttkb7SCoHugFri9zWzMxa\nSJ5hMR8YKmmwpPYkE9YzG/SZCVyYLo8HHo+ISNsnpldLDQaGAs/lWKuZmTUht2GodA7iUmA2UAbc\nFRELJV0DVEXETOBO4F5JS4B1JIFC2m8G8DJQB3wmIurzqtUa5eE9K2X+/mxhSn6RNzMz2z3fwW1m\nZpkcFmZmlslhYWZmmfbr+yys+Ug6iuTO+R03P64CZkbEotarysxKhc8sDElfInkci0guUX4uXX6g\nsQdAmpUKSRe1dg0HCl8NZUj6EzAiIrY1aG8PLIyIoa1TmVnTJK2IiIGtXceBwMNQBrAdOAx4tUH7\noek6s1Yj6cXdrQL6tmQtBzKHhQF8DvhfSX/mrYc/DgSGAJe2WlVmib7AB4H1DdoFPN3y5RyYHBZG\nRDwqaRjJY+ALJ7jn+855KwG/ALpExIKGKyQ92fLlHJg8Z2FmZpl8NZSZmWVyWJiZWSaHhdluSBok\n6Q857t+Ts7bfcFiYtZKIGNPaNZgVy2Fh1rQySf8jaaGkX0nqJGmkpGclvSjpIUk9ILkyR1Jlutxb\n0vJ0eYSk5yQtSLcZmrZvSv9+b7rtg5L+KOlHSj+8WdIZadvzkm6R9ItW+VewA57DwqxpQ4FbI2IE\nsAH4KPBD4EsRcSzwEvCVjH18Crg5IkYClUB1I32OI7nfZThwOHCSpI7AHcDpEfEuoE8zvB+zveKw\nMGvasoLr+58HjgC6R8TctO0e4JSMfTwDXJk+g+udEbG5kT7PRUR1RGwHFgCDgKOApRGxLO3zwD68\nD7N94rAwa9qWguV6oHsTfet46/9Uxx2NEXE/cDawGZgl6X1FHMc3zFpJcViY7ZmNwHpJ70lfXwDs\nOMtYDrwrXR6/YwNJh5OcIdwC/Bw4tshjLQYOlzQofX3eXldtto/824vZnrsQuF3SQcBSYMdjsq8H\nZkiaAjxS0P9c4AJJ24C/AtcWc5CI2Czp08Cjkt4E5jfXGzDbU37ch1kJk9QlIjalV0fdCvw5Im5s\n7brswONhKLPS9klJC4CFQDeSq6PMWpzPLMzMLJPPLMzMLJPDwszMMjkszMwsk8PCzMwyOSzMzCyT\nw8LMzDL9f5itN1XZDehHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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89D2aarbuvO0RJ9B34jfoO/Eb7HVq5nnfJfsdAsD2eb+ix6Bh9BzxMbY9fhcA9SueJ7Zv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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Zm5mZ5c53cJuZWSqHhZmZpXJYmJlZKoeFmZmlcliYmVkqh4WZmaVyWJiZWSqH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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TNrrgcRVwWsYxrsmlODMzK1pzneA2M7NmxGFhZmaZHBZmZpbJYWFmZpkcFmZm\nlslhYWZmmRwWZmaWyWFhZmaZHBZmZpbJYWFmZpkcFmZmlslhYWZmmRwWZmaWyWFhZmaZHBZmZpbJ\nYWFmZpkcFmZmlslhYWZmmRwWZmaWyWFhZmaZHBZmZpYp17CQNFTSG5LmSLqinu3tJE1It0+V1Ddt\nP1bSS5JeS3/9hzzrNDOzhuUWFpJKgduB44CBwEhJA+t0Oxf4MCL6AzcDP0zblwInRcSBwNnA2Lzq\nNDOzbHmeWQwB5kTE3IioBsYDw+r0GQbclz5+EDhGkiLilYh4J22fCbSX1C7HWs3MrAF5hkVPYGHB\n80VpW719IqIWWAl0q9Pnq8DLEbEhpzrNzCxDm6YuoCGSDiAZmvryNrafB5wH0KdPn0aszMysdcnz\nzGIx0Lvgea+0rd4+ktoAnYFl6fNewMPAWRHxVn0vEBF3RURFRFT06NFjB5dvZmab5RkW04ABkvpJ\nagucAUyq02cSyQQ2wHDg6YgISbsBjwNXRMTzOdZoZmZFyC0s0jmIi4DJwGxgYkTMlHStpJPTbncD\n3STNAb4FbP567UVAf2C0pBnpz2fyqtXMzBqW65xFRFQClXXaRhc8rgJOq2e/7wHfy7M2MzMrnq/g\nNjOzTA4LMzPL5LAwM7NMDgszM8vksDAzs0wOCzMzy+SwMDOzTA4LMzPL5LAwM7NMDgszM8vksDAz\ns0wOCzMzy+SwMDOzTA4LMzPL5LAwM7NMDgszM8vksDAzs0wOCzMzy+SwMDOzTA4LMzPL5LAwM7NM\nuYaFpKGS3pA0R9IV9WxvJ2lCun2qpL4F265M29+Q9JU86zQzs4blFhaSSoHbgeOAgcBISQPrdDsX\n+DAi+gM3Az9M9x0InAEcAAwF7kiPZ2ZmTSDPM4shwJyImBsR1cB4YFidPsOA+9LHDwLHSFLaPj4i\nNkTEPGBOejwzM2sCbXI8dk9gYcHzRcAh2+oTEbWSVgLd0vYX6+zbs+4LSDoPOC99ukbSGzum9HzV\nXHZ6U5dQjO7A0qYuohjJ/y9sR/Hfzx1nJ/m7uWcxnfIMi9xFxF3AXU1dR0skaXpEVDR1HWb18d/P\nxpfnMNRioHfB815pW719JLUBOgPLitzXzMwaSZ5hMQ0YIKmfpLYkE9aT6vSZBJydPh4OPB0Rkbaf\nkX5bqh8wAPhTjrWamVkDchuGSucgLgImA6XAmIiYKelaYHpETALuBsZKmgMsJwkU0n4TgVlALfCN\niNiYV61WLw/vWXPmv5+NTMl/5M3MzLbNV3CbmVkmh4WZmWVyWJiZWaad+joL23Ek7Udy5fzmix8X\nA5MiYnbTVWW25e9mT2BqRKxo2qxAAAACU0lEQVQpaB8aEU80XWWti88sDEnfIVmORSRfUf5T+nhc\nfQtAmjUWSRcDjwLfBP4iqXDJoOuapqrWyd+GMiS9CRwQETV12tsCMyNiQNNUZq2dpNeAQyNiTboq\n9YPA2Ii4RdIrEfH5Ji2wFfEwlAFsAvYA3q7T/rl0m1lTKdk89BQR8yV9EXhQ0p4kZ7/WSBwWBnAJ\n8JSkv/LR4o99gP7ARU1WlRm8L2lwRMwASM8wTgTGAAc2bWmti4ehDABJJSTLwBdOcE/zlfPWlCT1\nAmoj4r16th0eEc83QVmtksPCzMwy+dtQZmaWyWFhZmaZHBZmjUjSHpIeTB8PlnR8Eft8UdL/5F+d\n2bY5LMwaiaQ2EfFORAxPmwYDmWFh1hw4LMwySOor6XVJ90p6U9KvJX1J0vOS/ippSPrzgqRXJP1R\n0r7pvqMkTZL0NMnXk/tK+kt6weO1wAhJMySN2NYxzJoDX2dhVpz+wGnAP5HcBfJrwBHAycBVwFnA\n36c3/foSyVIUX033PQgYFBHL06uQiYhqSaOBioi4CEDSrg0cw6xJOSzMijMvIl4DkDQTeCoiIl2O\noi/J/ePvkzQACKCsYN8nI2J5Ea/R0DHMmpSHocyKs6Hg8aaC55tI/tP1XeCZiPgb4CSgvKD/2iJf\no6FjmDUph4XZjtGZ5Kp3gFFF7rMa6PQpj2HWKBwWZjvGDcAPJL1C8cO7zwADN09wf8JjmDUKL/dh\nZmaZfGZhZmaZHBZmZpbJYWFmZpkcFmZmlslhYWZmmRwWZmaWyWFhZmaZHBZmZpbp/wDgAMst8WTw\nnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NXL6ZmW2XZVjMAYZKGiypI3AWML1en+nkJrABxgOPR0RI2huYAVwaEU9nWKOZ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\ngg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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v/o7qpa+Q0vNQuk++jpTuvSJaCxHparSlEBFLSyf3ojvoccl99PinX5N26BBI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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mzpzJww8/zC9/+cu6ZZ06deLQQw/lgQce4KSTTmpwG1OnTuWpp56qm7/llls4+OCDP3bb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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_color = '#058caa'\n",
    "num_color = '#ed8549'\n",
    "\n",
    "final_iv,_ = data_vars(df1,df1['target'])\n",
    "final_iv = final_iv[(final_iv.VAR_NAME != 'target')]\n",
    "grouped = final_iv.groupby(['VAR_NAME'])\n",
    "for key, group in grouped:\n",
    "    ax = group.plot('MIN_VALUE','EVENT_RATE',kind='bar',color=bar_color,linewidth=1.0,edgecolor=['black'])\n",
    "    ax.set_title(str(key) + \" vs \" + str('target'))\n",
    "    ax.set_xlabel(key)\n",
    "    ax.set_ylabel(str('target') + \" %\")\n",
    "    rects = ax.patches\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.text(rect.get_x()+rect.get_width()/2., 1.01*height, str(round(height*100,1)) + '%', \n",
    "                ha='center', va='bottom', color=num_color, fontweight='bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mbagav200/anaconda/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "train, test = train_test_split(df1, test_size = 0.4)\n",
    "train = train.reset_index(drop=True)\n",
    "test = test.reset_index(drop=True)\n",
    "\n",
    "features_train = train[list(vif['Features'])]\n",
    "label_train = train['target']\n",
    "features_test = test[list(vif['Features'])]\n",
    "label_test = test['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9896755162241888 0.8944168048645661 0.9996797140804294 0.8602791800985341\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "clf.fit(features_train,label_train)\n",
    "\n",
    "pred_train = clf.predict(features_train)\n",
    "pred_test = clf.predict(features_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_train = accuracy_score(pred_train,label_train)\n",
    "accuracy_test = accuracy_score(pred_test,label_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_train), clf.predict_proba(features_train)[:,1])\n",
    "auc_train = metrics.auc(fpr,tpr)\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_test), clf.predict_proba(features_test)[:,1])\n",
    "auc_test = metrics.auc(fpr,tpr)\n",
    "\n",
    "print(accuracy_train,accuracy_test,auc_train,auc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2395</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRED       0    1\n",
       "ACTUAL           \n",
       "0       2395    1\n",
       "1         27  289"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_train,pd.Series(pred_train),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1567</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>154</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRED       0   1\n",
       "ACTUAL          \n",
       "0       1567  37\n",
       "1        154  51"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_test,pd.Series(pred_test),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8849557522123894 0.8988391376451078 0.8474989962173243 0.8627942339273766\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression()\n",
    "\n",
    "clf.fit(features_train,label_train)\n",
    "\n",
    "pred_train = clf.predict(features_train)\n",
    "pred_test = clf.predict(features_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_train = accuracy_score(pred_train,label_train)\n",
    "accuracy_test = accuracy_score(pred_test,label_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_train), clf.predict_proba(features_train)[:,1])\n",
    "auc_train = metrics.auc(fpr,tpr)\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_test), clf.predict_proba(features_test)[:,1])\n",
    "auc_test = metrics.auc(fpr,tpr)\n",
    "\n",
    "print(accuracy_train,accuracy_test,auc_train,auc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2346</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>241</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRED       0   1\n",
       "ACTUAL          \n",
       "0       2346  50\n",
       "1        241  75"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_train,pd.Series(pred_train),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1565</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>169</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRED       0   1\n",
       "ACTUAL          \n",
       "0       1565  39\n",
       "1        169  36"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_test,pd.Series(pred_test),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8849557522123894 0.8833609729132117 0.654527059867712 0.6470439754272854\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "clf = MLPClassifier()\n",
    "\n",
    "clf.fit(features_train,label_train)\n",
    "\n",
    "pred_train = clf.predict(features_train)\n",
    "pred_test = clf.predict(features_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_train = accuracy_score(pred_train,label_train)\n",
    "accuracy_test = accuracy_score(pred_test,label_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_train), clf.predict_proba(features_train)[:,1])\n",
    "auc_train = metrics.auc(fpr,tpr)\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_test), clf.predict_proba(features_test)[:,1])\n",
    "auc_test = metrics.auc(fpr,tpr)\n",
    "\n",
    "print(accuracy_train,accuracy_test,auc_train,auc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2358</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>274</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRED       0   1\n",
       "ACTUAL          \n",
       "0       2358  38\n",
       "1        274  42"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_train,pd.Series(pred_train),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1571</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>178</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRED       0   1\n",
       "ACTUAL          \n",
       "0       1571  33\n",
       "1        178  27"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_test,pd.Series(pred_test),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.870575221238938 0.8606965174129353 0.8375549438938316 0.8113770451918983\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB \n",
    "clf = GaussianNB()\n",
    "\n",
    "clf.fit(features_train,label_train)\n",
    "\n",
    "pred_train = clf.predict(features_train)\n",
    "pred_test = clf.predict(features_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_train = accuracy_score(pred_train,label_train)\n",
    "accuracy_test = accuracy_score(pred_test,label_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_train), clf.predict_proba(features_train)[:,1])\n",
    "auc_train = metrics.auc(fpr,tpr)\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_test), clf.predict_proba(features_test)[:,1])\n",
    "auc_test = metrics.auc(fpr,tpr)\n",
    "\n",
    "print(accuracy_train,accuracy_test,auc_train,auc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2216</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>171</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRED       0    1\n",
       "ACTUAL           \n",
       "0       2216  180\n",
       "1        171  145"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_train,pd.Series(pred_train),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1479</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>127</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRED       0    1\n",
       "ACTUAL           \n",
       "0       1479  125\n",
       "1        127   78"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_test,pd.Series(pred_test),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9387905604719764 0.8833609729132117 0.9588587783436531 0.8869214159722644\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "clf = GradientBoostingClassifier()\n",
    "\n",
    "clf.fit(features_train,label_train)\n",
    "\n",
    "pred_train = clf.predict(features_train)\n",
    "pred_test = clf.predict(features_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_train = accuracy_score(pred_train,label_train)\n",
    "accuracy_test = accuracy_score(pred_test,label_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_train), clf.predict_proba(features_train)[:,1])\n",
    "auc_train = metrics.auc(fpr,tpr)\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_test), clf.predict_proba(features_test)[:,1])\n",
    "auc_test = metrics.auc(fpr,tpr)\n",
    "\n",
    "print(accuracy_train,accuracy_test,auc_train,auc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2369</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>139</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRED       0    1\n",
       "ACTUAL           \n",
       "0       2369   27\n",
       "1        139  177"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_train,pd.Series(pred_train),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1535</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>142</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRED       0   1\n",
       "ACTUAL          \n",
       "0       1535  69\n",
       "1        142  63"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_test,pd.Series(pred_test),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Performance Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest - Hyper parameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 10 candidates, totalling 20 fits\n",
      "[CV] n_estimators=10, max_depth=None, max_features=sqrt, min_samples_split=5, bootstrap=True, min_samples_leaf=4 \n",
      "[CV] n_estimators=10, max_depth=None, max_features=sqrt, min_samples_split=5, bootstrap=True, min_samples_leaf=4 \n",
      "[CV] n_estimators=10, max_depth=None, max_features=sqrt, min_samples_split=10, bootstrap=True, min_samples_leaf=1 \n",
      "[CV] n_estimators=10, max_depth=None, max_features=sqrt, min_samples_split=10, bootstrap=True, min_samples_leaf=1 \n",
      "[CV] n_estimators=227, max_depth=3, max_features=auto, min_samples_split=10, bootstrap=True, min_samples_leaf=2 \n",
      "[CV] n_estimators=227, max_depth=3, max_features=auto, min_samples_split=10, bootstrap=True, min_samples_leaf=2 \n",
      "[CV] n_estimators=445, max_depth=None, max_features=auto, min_samples_split=5, bootstrap=True, min_samples_leaf=1 \n",
      "[CV]  n_estimators=10, max_depth=None, max_features=sqrt, min_samples_split=5, bootstrap=True, min_samples_leaf=4, total=   0.0s\n",
      "[CV]  n_estimators=10, max_depth=None, max_features=sqrt, min_samples_split=5, bootstrap=True, min_samples_leaf=4, total=   0.0s\n",
      "[CV] n_estimators=445, max_depth=None, max_features=auto, min_samples_split=5, bootstrap=True, min_samples_leaf=1 \n",
      "[CV] n_estimators=173, max_depth=3, max_features=sqrt, min_samples_split=2, bootstrap=False, min_samples_leaf=1 \n",
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      "[CV] n_estimators=282, max_depth=3, max_features=auto, min_samples_split=2, bootstrap=True, min_samples_leaf=4 \n",
      "[CV] n_estimators=282, max_depth=3, max_features=auto, min_samples_split=2, bootstrap=True, min_samples_leaf=4 \n",
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      "[CV] n_estimators=173, max_depth=3, max_features=auto, min_samples_split=2, bootstrap=True, min_samples_leaf=4 \n",
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      "[CV]  n_estimators=227, max_depth=3, max_features=auto, min_samples_split=10, bootstrap=True, min_samples_leaf=2, total=   0.8s\n",
      "[CV] n_estimators=282, max_depth=3, max_features=sqrt, min_samples_split=10, bootstrap=True, min_samples_leaf=2 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   5 out of  20 | elapsed:    0.7s remaining:    2.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=227, max_depth=3, max_features=auto, min_samples_split=10, bootstrap=True, min_samples_leaf=2, total=   0.8s\n",
      "[CV] n_estimators=282, max_depth=3, max_features=sqrt, min_samples_split=10, bootstrap=True, min_samples_leaf=2 \n",
      "[CV]  n_estimators=282, max_depth=3, max_features=auto, min_samples_split=2, bootstrap=True, min_samples_leaf=4, total=   0.9s\n",
      "[CV]  n_estimators=282, max_depth=3, max_features=auto, min_samples_split=2, bootstrap=True, min_samples_leaf=4, total=   0.9s\n",
      "[CV] n_estimators=118, max_depth=None, max_features=sqrt, min_samples_split=10, bootstrap=True, min_samples_leaf=1 \n",
      "[CV]  n_estimators=173, max_depth=3, max_features=auto, min_samples_split=2, bootstrap=True, min_samples_leaf=4, total=   0.6s\n",
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      "[CV]  n_estimators=118, max_depth=None, max_features=sqrt, min_samples_split=10, bootstrap=True, min_samples_leaf=1, total=   0.4s\n",
      "[CV] n_estimators=118, max_depth=None, max_features=sqrt, min_samples_split=10, bootstrap=True, min_samples_leaf=1 \n",
      "[CV]  n_estimators=282, max_depth=3, max_features=sqrt, min_samples_split=10, bootstrap=True, min_samples_leaf=2, total=   0.8s\n",
      "[CV]  n_estimators=282, max_depth=3, max_features=sqrt, min_samples_split=10, bootstrap=True, min_samples_leaf=2, total=   0.8s\n",
      "[CV]  n_estimators=445, max_depth=None, max_features=auto, min_samples_split=5, bootstrap=True, min_samples_leaf=1, total=   1.7s\n",
      "[CV]  n_estimators=445, max_depth=None, max_features=auto, min_samples_split=5, bootstrap=True, min_samples_leaf=1, total=   1.8s\n",
      "[CV]  n_estimators=118, max_depth=None, max_features=sqrt, min_samples_split=10, bootstrap=True, min_samples_leaf=1, total=   0.3s\n",
      "[CV] n_estimators=10, max_depth=3, max_features=auto, min_samples_split=2, bootstrap=False, min_samples_leaf=2 \n",
      "[CV]  n_estimators=10, max_depth=3, max_features=auto, min_samples_split=2, bootstrap=False, min_samples_leaf=2, total=   0.0s\n",
      "[CV] n_estimators=10, max_depth=3, max_features=auto, min_samples_split=2, bootstrap=False, min_samples_leaf=2 \n",
      "[CV]  n_estimators=10, max_depth=3, max_features=auto, min_samples_split=2, bootstrap=False, min_samples_leaf=2, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  16 out of  20 | elapsed:    1.9s remaining:    0.5s\n",
      "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:    2.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 118, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_split': 10, 'bootstrap': True, 'min_samples_leaf': 1}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "n_estimators = [int(x) for x in np.linspace(start = 10, stop = 500, num = 10)]\n",
    "max_features = ['auto', 'sqrt']\n",
    "max_depth = [int(x) for x in np.linspace(3, 10, num = 1)]\n",
    "max_depth.append(None)\n",
    "min_samples_split = [2, 5, 10]\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "bootstrap = [True, False]\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 10, cv = 2, verbose=2, random_state=42, n_jobs = -1)\n",
    "rf_random.fit(features_train, label_train)\n",
    "\n",
    "print(rf_random.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9627581120943953 0.8861249309010503 0.9957180744278438 0.8905632260811386\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier(**rf_random.best_params_)\n",
    "\n",
    "clf.fit(features_train,label_train)\n",
    "\n",
    "pred_train = clf.predict(features_train)\n",
    "pred_test = clf.predict(features_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_train = accuracy_score(pred_train,label_train)\n",
    "accuracy_test = accuracy_score(pred_test,label_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_train), clf.predict_proba(features_train)[:,1])\n",
    "auc_train = metrics.auc(fpr,tpr)\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_test), clf.predict_proba(features_test)[:,1])\n",
    "auc_test = metrics.auc(fpr,tpr)\n",
    "\n",
    "print(accuracy_train,accuracy_test,auc_train,auc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting - Hyper parameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 360 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mbagav200/anaconda/lib/python3.5/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=1, min_samples_split=2 \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.5s\n",
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      "[CV] max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   1.0s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.9s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.5s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
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      "[CV] max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.7s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   0.3s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.7s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.6s\n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=2 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV] max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.9s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   0.4s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.8s\n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.9s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV] max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   1.1s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.7s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.7s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   1.0s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   0.6s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.8s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=2 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.0s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.0s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.7s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   1.1s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.1s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.0s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   0.6s\n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV] max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.3s\n",
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      "[CV] max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.3s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   1.0s\n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   1.1s\n",
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      "[CV] max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
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      "[CV] max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   0.7s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.4s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.8s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   1.1s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.3s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   1.1s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.4s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.9s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.3s\n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   0.7s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV] max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   1.1s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.8s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.6s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.4s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.5s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   0.8s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.4s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.5s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.6s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.9s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.7s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   0.8s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.7s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   1.2s\n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   1.1s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV] max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.9s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  88 tasks      | elapsed:    9.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.3s\n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.7s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV] max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.0s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.0s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.1s\n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.7s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV] max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.3s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   0.9s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.6s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
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      "[CV]  max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   1.1s\n",
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      "[CV] max_features=auto, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   1.0s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
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      "[CV] max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
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      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=1, min_samples_split=2 \n",
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      "[CV] max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=2 \n",
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      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=2 \n",
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      "[CV]  max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=2 -   0.5s\n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=2 -   0.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=2 \n",
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      "[CV]  max_features=auto, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   1.0s\n",
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      "[CV] max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.6s\n",
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      "[CV]  max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.5s\n",
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      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV]  max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.7s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.1s\n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.8s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.1s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.9s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.9s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.7s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV]  max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.4s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=1, min_samples_split=5 -   0.8s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.5s\n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   1.3s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.5s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.6s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.0s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.0s\n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.6s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.1s\n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.7s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.1s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.7s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.2s\n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=2 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=1, min_samples_split=10 -   0.7s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
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      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=2, min_samples_split=2 \n",
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      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
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      "[CV] max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.0s\n",
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      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
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      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
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      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=2, min_samples_split=5 -   0.8s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
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      "[CV] max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=10 \n",
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      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
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      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
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      "[CV] max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
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      "[CV] max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
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      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
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      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.1s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.7s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.8s\n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.8s\n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.8s\n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=2, min_samples_split=10 -   0.9s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.4s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.4s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 307 tasks      | elapsed:   21.7s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.5s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.5s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.6s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
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      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.6s\n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.0s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
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      "[CV]  max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.1s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.1s\n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.7s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.7s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.8s\n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.8s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
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      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.9s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=2 -   0.9s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.4s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.4s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.5s\n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.5s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.6s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.6s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.0s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.0s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.1s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.1s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.7s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.7s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.8s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.8s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.9s\n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=5 -   0.9s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.4s\n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.4s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.5s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.5s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.6s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.6s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.7s\n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.7s\n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.8s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.8s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.9s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=3, min_samples_leaf=4, min_samples_split=10 -   0.8s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.0s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.3s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.4s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.2s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.4s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.3s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.4s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.5s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.4s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.5s\n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.6s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.5s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.6s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.5s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.6s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   1.5s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   1.7s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   1.7s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   1.9s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.0s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   1.8s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   1.8s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.1s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   1.9s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   0.2s\n",
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      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   1.8s\n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.1s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.4s\n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.4s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.4s\n",
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      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
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      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
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      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
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      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
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      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
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      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
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      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
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      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.1s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
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      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.5s\n",
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      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.3s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.5s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
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      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
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      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.7s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.7s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.6s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.8s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.5s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.7s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.6s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.7s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.8s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.7s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.6s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   1.6s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.8s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   1.7s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   2.7s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   2.8s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.0s\n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.1s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   2.9s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   2.9s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   2.9s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   2.9s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   2.9s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   2.9s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.1s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   1.9s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   1.9s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   2.8s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   2.7s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   2.8s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   2.9s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 510 tasks      | elapsed:  1.1min\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   2.9s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   0.2s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.1s\n",
      "[CV] max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   1.8s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   1.7s\n",
      "[CV] max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.7s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.7s\n",
      "[CV] max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.8s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.8s\n",
      "[CV] max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.8s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   3.0s\n",
      "[CV] max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   3.1s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   3.1s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   3.1s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.1s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.3s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   3.2s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=auto, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   3.2s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.4s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.4s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.4s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.4s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.5s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.5s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.5s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.5s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.5s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.6s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.6s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.6s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.7s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.7s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=2 -   1.7s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.1s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.1s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.4s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.5s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.5s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.6s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.6s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.6s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.7s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   1.7s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   1.8s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=5 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.5s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.5s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.6s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.6s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.6s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.7s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.7s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=1, min_samples_split=10 -   2.7s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.3s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.3s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.7s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.7s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   3.1s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   0.4s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   3.1s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   3.3s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   3.4s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   3.4s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   3.5s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   2.6s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=2 -   3.6s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   2.6s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.2s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.2s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.3s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.3s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.3s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.4s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.4s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.4s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.6s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.6s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.7s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.6s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.7s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.7s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.0s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=5 -   3.8s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.0s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   3.1s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   3.1s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   3.2s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   3.2s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   3.2s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=2, min_samples_split=10 -   3.1s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   2.0s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   2.0s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.0s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.1s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.2s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.2s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.3s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.3s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.4s\n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.3s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=2 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.5s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   0.3s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.6s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.7s\n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.6s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.7s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.6s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.6s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=2 -   3.6s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   1.9s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   1.9s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.2s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.4s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.5s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.5s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.6s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.7s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=5 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   4.0s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   4.0s\n",
      "[CV] max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=10, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   0.2s\n",
      "[CV] max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.8s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.7s\n",
      "[CV] max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.8s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.7s\n",
      "[CV] max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   1.9s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.8s\n",
      "[CV] max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=64, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   1.9s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=5 -   3.9s\n",
      "[CV] max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=118, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=173, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_features=sqrt, n_estimators=227, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.8s\n",
      "[CV] max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.7s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=282, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.9s\n",
      "[CV] max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=10 \n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.8s\n",
      "[CV]  max_features=sqrt, n_estimators=336, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.8s\n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.8s\n",
      "[CV]  max_features=sqrt, n_estimators=391, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.8s\n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.4s\n",
      "[CV]  max_features=sqrt, n_estimators=445, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.4s\n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.4s\n",
      "[CV]  max_features=sqrt, n_estimators=500, max_depth=None, min_samples_leaf=4, min_samples_split=10 -   2.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 720 out of 720 | elapsed:  2.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_samples_leaf': 4, 'n_estimators': 118, 'max_depth': 3, 'max_features': 'sqrt', 'min_samples_split': 10}\n"
     ]
    }
   ],
   "source": [
    "from sklearn import cross_validation, metrics   \n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "n_estimators = [int(x) for x in np.linspace(start = 10, stop = 500, num = 10)]\n",
    "max_features = ['auto', 'sqrt']\n",
    "max_depth = [int(x) for x in np.linspace(3, 10, num = 1)]\n",
    "max_depth.append(None)\n",
    "min_samples_split = [2, 5, 10]\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "\n",
    "grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "gb = GradientBoostingClassifier()\n",
    "\n",
    "gf_tune = GridSearchCV(estimator = gb, param_grid = grid, cv = 2, verbose=2, n_jobs = -1)\n",
    "gf_tune.fit(features_train, label_train)\n",
    "\n",
    "print(gf_tune.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_samples_leaf': 4, 'n_estimators': 118, 'max_depth': 3, 'max_features': 'sqrt', 'min_samples_split': 10}\n"
     ]
    }
   ],
   "source": [
    "print(gf_tune.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9351032448377581 0.892758430071863 0.9542347742017285 0.8909981144699228\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "clf = GradientBoostingClassifier(**gf_tune.best_params_)\n",
    "\n",
    "clf.fit(features_train,label_train)\n",
    "\n",
    "pred_train = clf.predict(features_train)\n",
    "pred_test = clf.predict(features_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_train = accuracy_score(pred_train,label_train)\n",
    "accuracy_test = accuracy_score(pred_test,label_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_train), clf.predict_proba(features_train)[:,1])\n",
    "auc_train = metrics.auc(fpr,tpr)\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_test), clf.predict_proba(features_test)[:,1])\n",
    "auc_test = metrics.auc(fpr,tpr)\n",
    "\n",
    "print(accuracy_train,accuracy_test,auc_train,auc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Final Model and Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8934365781710915 0.8850193477059148 0.8690354176792544 0.8449881394075786\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression()\n",
    "\n",
    "clf.fit(features_train,label_train)\n",
    "\n",
    "pred_train = clf.predict(features_train)\n",
    "pred_test = clf.predict(features_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_train = accuracy_score(pred_train,label_train)\n",
    "accuracy_test = accuracy_score(pred_test,label_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_train), clf.predict_proba(features_train)[:,1])\n",
    "auc_train = metrics.auc(fpr,tpr)\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_test), clf.predict_proba(features_test)[:,1])\n",
    "auc_test = metrics.auc(fpr,tpr)\n",
    "\n",
    "print(accuracy_train,accuracy_test,auc_train,auc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2350</td>\n",
       "      <td>46</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>243</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "PRED       0   1\n",
       "ACTUAL          \n",
       "0       2350  46\n",
       "1        243  73"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_train,pd.Series(pred_train),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1565</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>169</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRED       0   1\n",
       "ACTUAL          \n",
       "0       1565  39\n",
       "1        169  36"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(label_test,pd.Series(pred_test),rownames=['ACTUAL'],colnames=['PRED'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"53dcc69c-4355-4e63-8c84-9e213f38db48\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
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       "\n",
       "  var force = \"1\";\n",
       "\n",
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       "    window._bokeh_onload_callbacks = [];\n",
       "    window._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "\n",
       "  \n",
       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    if (window.Bokeh !== undefined) {\n",
       "      Bokeh.$(\"#53dcc69c-4355-4e63-8c84-9e213f38db48\").text(\"BokehJS successfully loaded.\");\n",
       "    } else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"53dcc69c-4355-4e63-8c84-9e213f38db48\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '53dcc69c-4355-4e63-8c84-9e213f38db48' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.2.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.2.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.12.2.min.js'];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      Bokeh.$(\"#53dcc69c-4355-4e63-8c84-9e213f38db48\").text(\"BokehJS is loading...\");\n",
       "\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.2.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.2.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.2.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.2.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === \"1\")) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === \"1\") {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (!force) {\n",
       "      var cell = $(\"#53dcc69c-4355-4e63-8c84-9e213f38db48\").parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
       "\n",
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       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
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       "\n",
       "    var force = \"\";\n",
       "\n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "\n",
       "\n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "\n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "\n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        Bokeh.$(\"#f37a91ae-e010-462a-bdad-a35c1c9a6305\").text(\"BokehJS successfully loaded.\");\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "\n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"f37a91ae-e010-462a-bdad-a35c1c9a6305\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'f37a91ae-e010-462a-bdad-a35c1c9a6305' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "\n",
       "    var js_urls = [];\n",
       "\n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
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       "            var render_items = [{\"docid\":\"701bdd33-4145-4996-a6ed-2319cccd6cb6\",\"elementid\":\"f37a91ae-e010-462a-bdad-a35c1c9a6305\",\"modelid\":\"a290a796-6320-4f8f-97c5-065ab0ba3be4\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
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       "      },\n",
       "      function(Bokeh) {\n",
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       "\n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === \"1\")) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === \"1\") {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (!force) {\n",
       "        var cell = $(\"#f37a91ae-e010-462a-bdad-a35c1c9a6305\").parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
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       "\n",
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       "\n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
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       "    }\n",
       "  }(this));\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.charts import Histogram\n",
    "from ipywidgets import interact\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.io import push_notebook, show, output_notebook\n",
    "output_notebook()\n",
    "\n",
    "from sklearn import metrics\n",
    "preds = clf.predict_proba(features_train)[:,1]\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_train), preds)\n",
    "auc = metrics.auc(fpr,tpr)\n",
    "\n",
    "p = figure(title=\"ROC Curve - Train data\")\n",
    "r = p.line(fpr,tpr,color='#0077bc',legend = 'AUC = '+ str(round(auc,3)), line_width=2)\n",
    "s = p.line([0,1],[0,1], color= '#d15555',line_dash='dotdash',line_width=2)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"2e584dd2-2278-4ffb-b70d-8e9bab2740cc\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = \"1\";\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "    window._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "\n",
       "  \n",
       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    if (window.Bokeh !== undefined) {\n",
       "      Bokeh.$(\"#2e584dd2-2278-4ffb-b70d-8e9bab2740cc\").text(\"BokehJS successfully loaded.\");\n",
       "    } else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"2e584dd2-2278-4ffb-b70d-8e9bab2740cc\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '2e584dd2-2278-4ffb-b70d-8e9bab2740cc' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.2.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.2.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.12.2.min.js'];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      Bokeh.$(\"#2e584dd2-2278-4ffb-b70d-8e9bab2740cc\").text(\"BokehJS is loading...\");\n",
       "\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.2.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.2.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.2.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.2.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === \"1\")) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === \"1\") {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (!force) {\n",
       "      var cell = $(\"#2e584dd2-2278-4ffb-b70d-8e9bab2740cc\").parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"plotdiv\" id=\"d73ecdac-dc66-4ad8-8a4d-36a62eb87b4c\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
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       "\n",
       "    var force = \"\";\n",
       "\n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "\n",
       "\n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "\n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "\n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        Bokeh.$(\"#d73ecdac-dc66-4ad8-8a4d-36a62eb87b4c\").text(\"BokehJS successfully loaded.\");\n",
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       "    }\n",
       "\n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "\n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
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       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"d73ecdac-dc66-4ad8-8a4d-36a62eb87b4c\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'd73ecdac-dc66-4ad8-8a4d-36a62eb87b4c' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "\n",
       "    var js_urls = [];\n",
       "\n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
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       "            var render_items = [{\"docid\":\"09603227-ea06-4556-b1ab-b9219e656cca\",\"elementid\":\"d73ecdac-dc66-4ad8-8a4d-36a62eb87b4c\",\"modelid\":\"863000da-f0f5-499c-8f4c-10ff318decd2\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "\n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === \"1\")) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === \"1\") {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (!force) {\n",
       "        var cell = $(\"#d73ecdac-dc66-4ad8-8a4d-36a62eb87b4c\").parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "\n",
       "    }\n",
       "\n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.charts import Histogram\n",
    "from ipywidgets import interact\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.io import push_notebook, show, output_notebook\n",
    "output_notebook()\n",
    "\n",
    "from sklearn import metrics\n",
    "preds = clf.predict_proba(features_test)[:,1]\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(np.array(label_test), preds)\n",
    "auc = metrics.auc(fpr,tpr)\n",
    "\n",
    "p = figure(title=\"ROC Curve - Test data\")\n",
    "r = p.line(fpr,tpr,color='#0077bc',legend = 'AUC = '+ str(round(auc,3)), line_width=2)\n",
    "s = p.line([0,1],[0,1], color= '#d15555',line_dash='dotdash',line_width=2)\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decile Plots and KS statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "import pandas as pd\n",
    "import sys\n",
    "%matplotlib inline\n",
    "    \n",
    "def plot_pandas_style(styler):\n",
    "    from IPython.core.display import HTML\n",
    "    html = '\\n'.join([line.lstrip() for line in styler.render().split('\\n')])\n",
    "    return HTML(html)\n",
    "\n",
    "def highlight_max(s,color='yellow'):\n",
    "    '''\n",
    "    highlight the maximum in a Series yellow.\n",
    "    '''\n",
    "    is_max = s == s.max()\n",
    "    return ['background-color: {}'.format(color) if v else '' for v in is_max]\n",
    "\n",
    "def decile_labels(agg1,label,color='skyblue'):\n",
    "    agg_dummy = pd.DataFrame(OrderedDict((('TOTAL',0),('TARGET',0),('NONTARGET',0),('PCT_TAR',0),('CUM_TAR',0),('CUM_NONTAR',0),('DIST_TAR',0),('DIST_NONTAR',0),('SPREAD',0))),index=[0])\n",
    "    agg1 = agg1.append(agg_dummy).sort_index()\n",
    "    agg1.index.name = label\n",
    "    agg1 = agg1.style.apply(highlight_max, color = 'yellow', subset=['SPREAD'])\n",
    "    agg1.bar(subset=['TARGET'], color='{}'.format(color))\n",
    "    agg1.bar(subset=['TOTAL'], color='{}'.format(color))\n",
    "    agg1.bar(subset=['PCT_TAR'], color='{}'.format(color))\n",
    "    return(agg1)\n",
    "\n",
    "def deciling(data,decile_by,target,nontarget):\n",
    "    inputs = list(decile_by)\n",
    "    inputs.extend((target,nontarget))\n",
    "    decile = data[inputs]\n",
    "    grouped = decile.groupby(decile_by)\n",
    "    agg1 = pd.DataFrame({},index=[])\n",
    "    agg1['TOTAL'] = grouped.sum()[nontarget] + grouped.sum()[target]\n",
    "    agg1['TARGET'] = grouped.sum()[target]\n",
    "    agg1['NONTARGET'] = grouped.sum()[nontarget]\n",
    "    agg1['PCT_TAR'] = grouped.mean()[target]*100\n",
    "    agg1['CUM_TAR'] = grouped.sum()[target].cumsum()\n",
    "    agg1['CUM_NONTAR'] = grouped.sum()[nontarget].cumsum()\n",
    "    agg1['DIST_TAR'] = agg1['CUM_TAR']/agg1['TARGET'].sum()*100\n",
    "    agg1['DIST_NONTAR'] = agg1['CUM_NONTAR']/agg1['NONTARGET'].sum()*100\n",
    "    agg1['SPREAD'] = (agg1['DIST_TAR'] - agg1['DIST_NONTAR'])\n",
    "    agg1 = decile_labels(agg1,'DECILE',color='skyblue')\n",
    "    return(plot_pandas_style(agg1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def scoring(features,clf,target):\n",
    "    score = pd.DataFrame(clf.predict_proba(features)[:,1], columns = ['SCORE'])\n",
    "    score['DECILE'] = pd.qcut(score['SCORE'].rank(method = 'first'),10,labels=range(10,0,-1))\n",
    "    score['DECILE'] = score['DECILE'].astype(float)\n",
    "    score['TARGET'] = target\n",
    "    score['NONTARGET'] = 1 - target\n",
    "    return(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores_train = scoring(features_train,clf,label_train)\n",
    "scores_test = scoring(features_test,clf,label_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
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       "background:  linear-gradient(90deg,skyblue 99.6%, transparent 0%);\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row4_col1 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 15.4%, transparent 0%);\n",
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       "width:  10em;\n",
       "height:  80%;\n",
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       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 99.6%, transparent 0%);\n",
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       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 9.8%, transparent 0%);\n",
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       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 9.8%, transparent 0%);\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col0 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 99.6%, transparent 0%);\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col1 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 9.1%, transparent 0%);\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col3 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 9.1%, transparent 0%);\n",
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       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 99.6%, transparent 0%);\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row7_col1 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 2.1%, transparent 0%);\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row7_col3 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 2.1%, transparent 0%);\n",
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       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 99.6%, transparent 0%);\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row8_col1 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 4.2%, transparent 0%);\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row8_col3 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 4.2%, transparent 0%);\n",
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       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 99.6%, transparent 0%);\n",
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       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 0.7%, transparent 0%);\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row9_col3 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 0.7%, transparent 0%);\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col0 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "background:  linear-gradient(90deg,skyblue 100.0%, transparent 0%);\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col1 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "}    #T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col3 {\n",
       "width:  10em;\n",
       "height:  80%;\n",
       "}</style>  \n",
       "<table id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343\" > \n",
       "<thead>    <tr> \n",
       "<th class=\"blank level0\" ></th> \n",
       "<th class=\"col_heading level0 col0\" >TOTAL</th> \n",
       "<th class=\"col_heading level0 col1\" >TARGET</th> \n",
       "<th class=\"col_heading level0 col2\" >NONTARGET</th> \n",
       "<th class=\"col_heading level0 col3\" >PCT_TAR</th> \n",
       "<th class=\"col_heading level0 col4\" >CUM_TAR</th> \n",
       "<th class=\"col_heading level0 col5\" >CUM_NONTAR</th> \n",
       "<th class=\"col_heading level0 col6\" >DIST_TAR</th> \n",
       "<th class=\"col_heading level0 col7\" >DIST_NONTAR</th> \n",
       "<th class=\"col_heading level0 col8\" >SPREAD</th> \n",
       "</tr>    <tr> \n",
       "<th class=\"index_name level0\" >DECILE</th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "</tr></thead> \n",
       "<tbody>    <tr> \n",
       "<th id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343level0_row0\" class=\"row_heading level0 row0\" >0.0</th> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row0_col0\" class=\"data row0 col0\" >0</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row0_col1\" class=\"data row0 col1\" >0</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row0_col2\" class=\"data row0 col2\" >0</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row0_col3\" class=\"data row0 col3\" >0</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row0_col4\" class=\"data row0 col4\" >0</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row0_col5\" class=\"data row0 col5\" >0</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row0_col6\" class=\"data row0 col6\" >0</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row0_col7\" class=\"data row0 col7\" >0</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row0_col8\" class=\"data row0 col8\" >0</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343level0_row1\" class=\"row_heading level0 row1\" >1.0</th> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row1_col0\" class=\"data row1 col0\" >272</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row1_col1\" class=\"data row1 col1\" >143</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row1_col2\" class=\"data row1 col2\" >129</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row1_col3\" class=\"data row1 col3\" >52.5735</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row1_col4\" class=\"data row1 col4\" >143</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row1_col5\" class=\"data row1 col5\" >129</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row1_col6\" class=\"data row1 col6\" >45.2532</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row1_col7\" class=\"data row1 col7\" >5.38397</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row1_col8\" class=\"data row1 col8\" >39.8692</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343level0_row2\" class=\"row_heading level0 row2\" >2.0</th> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row2_col0\" class=\"data row2 col0\" >271</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row2_col1\" class=\"data row2 col1\" >75</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row2_col2\" class=\"data row2 col2\" >196</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row2_col3\" class=\"data row2 col3\" >27.6753</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row2_col4\" class=\"data row2 col4\" >218</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row2_col5\" class=\"data row2 col5\" >325</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row2_col6\" class=\"data row2 col6\" >68.9873</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row2_col7\" class=\"data row2 col7\" >13.5643</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row2_col8\" class=\"data row2 col8\" >55.4231</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343level0_row3\" class=\"row_heading level0 row3\" >3.0</th> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row3_col0\" class=\"data row3 col0\" >271</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row3_col1\" class=\"data row3 col1\" >39</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row3_col2\" class=\"data row3 col2\" >232</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row3_col3\" class=\"data row3 col3\" >14.3911</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row3_col4\" class=\"data row3 col4\" >257</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row3_col5\" class=\"data row3 col5\" >557</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row3_col6\" class=\"data row3 col6\" >81.3291</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row3_col7\" class=\"data row3 col7\" >23.2471</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row3_col8\" class=\"data row3 col8\" >58.082</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343level0_row4\" class=\"row_heading level0 row4\" >4.0</th> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row4_col0\" class=\"data row4 col0\" >271</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row4_col1\" class=\"data row4 col1\" >22</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row4_col2\" class=\"data row4 col2\" >249</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row4_col3\" class=\"data row4 col3\" >8.11808</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row4_col4\" class=\"data row4 col4\" >279</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row4_col5\" class=\"data row4 col5\" >806</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row4_col6\" class=\"data row4 col6\" >88.2911</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row4_col7\" class=\"data row4 col7\" >33.6394</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row4_col8\" class=\"data row4 col8\" >54.6517</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343level0_row5\" class=\"row_heading level0 row5\" >5.0</th> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row5_col0\" class=\"data row5 col0\" >271</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row5_col1\" class=\"data row5 col1\" >14</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row5_col2\" class=\"data row5 col2\" >257</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row5_col3\" class=\"data row5 col3\" >5.16605</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row5_col4\" class=\"data row5 col4\" >293</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row5_col5\" class=\"data row5 col5\" >1063</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row5_col6\" class=\"data row5 col6\" >92.7215</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row5_col7\" class=\"data row5 col7\" >44.3656</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row5_col8\" class=\"data row5 col8\" >48.3559</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343level0_row6\" class=\"row_heading level0 row6\" >6.0</th> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col0\" class=\"data row6 col0\" >271</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col1\" class=\"data row6 col1\" >13</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col2\" class=\"data row6 col2\" >258</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col3\" class=\"data row6 col3\" >4.79705</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col4\" class=\"data row6 col4\" >306</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col5\" class=\"data row6 col5\" >1321</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col6\" class=\"data row6 col6\" >96.8354</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col7\" class=\"data row6 col7\" >55.1336</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row6_col8\" class=\"data row6 col8\" >41.7019</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343level0_row7\" class=\"row_heading level0 row7\" >7.0</th> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row7_col0\" class=\"data row7 col0\" >271</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row7_col1\" class=\"data row7 col1\" >3</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row7_col2\" class=\"data row7 col2\" >268</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row7_col3\" class=\"data row7 col3\" >1.10701</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row7_col4\" class=\"data row7 col4\" >309</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row7_col5\" class=\"data row7 col5\" >1589</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row7_col6\" class=\"data row7 col6\" >97.7848</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row7_col7\" class=\"data row7 col7\" >66.3189</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row7_col8\" class=\"data row7 col8\" >31.4659</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343level0_row8\" class=\"row_heading level0 row8\" >8.0</th> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row8_col0\" class=\"data row8 col0\" >271</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row8_col1\" class=\"data row8 col1\" >6</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row8_col2\" class=\"data row8 col2\" >265</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row8_col3\" class=\"data row8 col3\" >2.21402</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row8_col4\" class=\"data row8 col4\" >315</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row8_col5\" class=\"data row8 col5\" >1854</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row8_col6\" class=\"data row8 col6\" >99.6835</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row8_col7\" class=\"data row8 col7\" >77.379</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row8_col8\" class=\"data row8 col8\" >22.3046</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343level0_row9\" class=\"row_heading level0 row9\" >9.0</th> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row9_col0\" class=\"data row9 col0\" >271</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row9_col1\" class=\"data row9 col1\" >1</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row9_col2\" class=\"data row9 col2\" >270</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row9_col3\" class=\"data row9 col3\" >0.369004</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row9_col4\" class=\"data row9 col4\" >316</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row9_col5\" class=\"data row9 col5\" >2124</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row9_col6\" class=\"data row9 col6\" >100</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row9_col7\" class=\"data row9 col7\" >88.6477</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row9_col8\" class=\"data row9 col8\" >11.3523</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343level0_row10\" class=\"row_heading level0 row10\" >10.0</th> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col0\" class=\"data row10 col0\" >272</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col1\" class=\"data row10 col1\" >0</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col2\" class=\"data row10 col2\" >272</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col3\" class=\"data row10 col3\" >0</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col4\" class=\"data row10 col4\" >316</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col5\" class=\"data row10 col5\" >2396</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col6\" class=\"data row10 col6\" >100</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col7\" class=\"data row10 col7\" >100</td> \n",
       "<td id=\"T_daea3e46_846a_11e8_afe4_a0999b1e1343row10_col8\" class=\"data row10 col8\" >0</td> \n",
       "</tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deciling(scores_train,['DECILE'],'TARGET','NONTARGET')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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       "<thead>    <tr> \n",
       "<th class=\"blank level0\" ></th> \n",
       "<th class=\"col_heading level0 col0\" >TOTAL</th> \n",
       "<th class=\"col_heading level0 col1\" >TARGET</th> \n",
       "<th class=\"col_heading level0 col2\" >NONTARGET</th> \n",
       "<th class=\"col_heading level0 col3\" >PCT_TAR</th> \n",
       "<th class=\"col_heading level0 col4\" >CUM_TAR</th> \n",
       "<th class=\"col_heading level0 col5\" >CUM_NONTAR</th> \n",
       "<th class=\"col_heading level0 col6\" >DIST_TAR</th> \n",
       "<th class=\"col_heading level0 col7\" >DIST_NONTAR</th> \n",
       "<th class=\"col_heading level0 col8\" >SPREAD</th> \n",
       "</tr>    <tr> \n",
       "<th class=\"index_name level0\" >DECILE</th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "<th class=\"blank\" ></th> \n",
       "</tr></thead> \n",
       "<tbody>    <tr> \n",
       "<th id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343level0_row0\" class=\"row_heading level0 row0\" >0.0</th> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row0_col0\" class=\"data row0 col0\" >0</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row0_col1\" class=\"data row0 col1\" >0</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row0_col2\" class=\"data row0 col2\" >0</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row0_col3\" class=\"data row0 col3\" >0</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row0_col4\" class=\"data row0 col4\" >0</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row0_col5\" class=\"data row0 col5\" >0</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row0_col6\" class=\"data row0 col6\" >0</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row0_col7\" class=\"data row0 col7\" >0</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row0_col8\" class=\"data row0 col8\" >0</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343level0_row1\" class=\"row_heading level0 row1\" >1.0</th> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row1_col0\" class=\"data row1 col0\" >181</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row1_col1\" class=\"data row1 col1\" >79</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row1_col2\" class=\"data row1 col2\" >102</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row1_col3\" class=\"data row1 col3\" >43.6464</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row1_col4\" class=\"data row1 col4\" >79</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row1_col5\" class=\"data row1 col5\" >102</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row1_col6\" class=\"data row1 col6\" >38.5366</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row1_col7\" class=\"data row1 col7\" >6.3591</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row1_col8\" class=\"data row1 col8\" >32.1775</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343level0_row2\" class=\"row_heading level0 row2\" >2.0</th> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row2_col0\" class=\"data row2 col0\" >181</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row2_col1\" class=\"data row2 col1\" >57</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row2_col2\" class=\"data row2 col2\" >124</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row2_col3\" class=\"data row2 col3\" >31.4917</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row2_col4\" class=\"data row2 col4\" >136</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row2_col5\" class=\"data row2 col5\" >226</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row2_col6\" class=\"data row2 col6\" >66.3415</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row2_col7\" class=\"data row2 col7\" >14.0898</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row2_col8\" class=\"data row2 col8\" >52.2517</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343level0_row3\" class=\"row_heading level0 row3\" >3.0</th> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row3_col0\" class=\"data row3 col0\" >181</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row3_col1\" class=\"data row3 col1\" >29</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row3_col2\" class=\"data row3 col2\" >152</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row3_col3\" class=\"data row3 col3\" >16.0221</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row3_col4\" class=\"data row3 col4\" >165</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row3_col5\" class=\"data row3 col5\" >378</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row3_col6\" class=\"data row3 col6\" >80.4878</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row3_col7\" class=\"data row3 col7\" >23.5661</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row3_col8\" class=\"data row3 col8\" >56.9217</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343level0_row4\" class=\"row_heading level0 row4\" >4.0</th> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row4_col0\" class=\"data row4 col0\" >181</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row4_col1\" class=\"data row4 col1\" >9</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row4_col2\" class=\"data row4 col2\" >172</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row4_col3\" class=\"data row4 col3\" >4.97238</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row4_col4\" class=\"data row4 col4\" >174</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row4_col5\" class=\"data row4 col5\" >550</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row4_col6\" class=\"data row4 col6\" >84.878</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row4_col7\" class=\"data row4 col7\" >34.2893</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row4_col8\" class=\"data row4 col8\" >50.5888</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343level0_row5\" class=\"row_heading level0 row5\" >5.0</th> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row5_col0\" class=\"data row5 col0\" >180</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row5_col1\" class=\"data row5 col1\" >12</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row5_col2\" class=\"data row5 col2\" >168</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row5_col3\" class=\"data row5 col3\" >6.66667</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row5_col4\" class=\"data row5 col4\" >186</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row5_col5\" class=\"data row5 col5\" >718</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row5_col6\" class=\"data row5 col6\" >90.7317</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row5_col7\" class=\"data row5 col7\" >44.7631</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row5_col8\" class=\"data row5 col8\" >45.9686</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343level0_row6\" class=\"row_heading level0 row6\" >6.0</th> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row6_col0\" class=\"data row6 col0\" >181</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row6_col1\" class=\"data row6 col1\" >9</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row6_col2\" class=\"data row6 col2\" >172</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row6_col3\" class=\"data row6 col3\" >4.97238</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row6_col4\" class=\"data row6 col4\" >195</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row6_col5\" class=\"data row6 col5\" >890</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row6_col6\" class=\"data row6 col6\" >95.122</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row6_col7\" class=\"data row6 col7\" >55.4863</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row6_col8\" class=\"data row6 col8\" >39.6357</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343level0_row7\" class=\"row_heading level0 row7\" >7.0</th> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row7_col0\" class=\"data row7 col0\" >181</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row7_col1\" class=\"data row7 col1\" >6</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row7_col2\" class=\"data row7 col2\" >175</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row7_col3\" class=\"data row7 col3\" >3.31492</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row7_col4\" class=\"data row7 col4\" >201</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row7_col5\" class=\"data row7 col5\" >1065</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row7_col6\" class=\"data row7 col6\" >98.0488</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row7_col7\" class=\"data row7 col7\" >66.3965</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row7_col8\" class=\"data row7 col8\" >31.6523</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343level0_row8\" class=\"row_heading level0 row8\" >8.0</th> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row8_col0\" class=\"data row8 col0\" >181</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row8_col1\" class=\"data row8 col1\" >1</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row8_col2\" class=\"data row8 col2\" >180</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row8_col3\" class=\"data row8 col3\" >0.552486</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row8_col4\" class=\"data row8 col4\" >202</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row8_col5\" class=\"data row8 col5\" >1245</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row8_col6\" class=\"data row8 col6\" >98.5366</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row8_col7\" class=\"data row8 col7\" >77.6185</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row8_col8\" class=\"data row8 col8\" >20.9181</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343level0_row9\" class=\"row_heading level0 row9\" >9.0</th> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row9_col0\" class=\"data row9 col0\" >181</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row9_col1\" class=\"data row9 col1\" >2</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row9_col2\" class=\"data row9 col2\" >179</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row9_col3\" class=\"data row9 col3\" >1.10497</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row9_col4\" class=\"data row9 col4\" >204</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row9_col5\" class=\"data row9 col5\" >1424</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row9_col6\" class=\"data row9 col6\" >99.5122</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row9_col7\" class=\"data row9 col7\" >88.7781</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row9_col8\" class=\"data row9 col8\" >10.7341</td> \n",
       "</tr>    <tr> \n",
       "<th id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343level0_row10\" class=\"row_heading level0 row10\" >10.0</th> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row10_col0\" class=\"data row10 col0\" >181</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row10_col1\" class=\"data row10 col1\" >1</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row10_col2\" class=\"data row10 col2\" >180</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row10_col3\" class=\"data row10 col3\" >0.552486</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row10_col4\" class=\"data row10 col4\" >205</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row10_col5\" class=\"data row10 col5\" >1604</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row10_col6\" class=\"data row10 col6\" >100</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row10_col7\" class=\"data row10 col7\" >100</td> \n",
       "<td id=\"T_daf835f8_846a_11e8_906b_a0999b1e1343row10_col8\" class=\"data row10 col8\" >0</td> \n",
       "</tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deciling(scores_test,['DECILE'],'TARGET','NONTARGET')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lift chart, Actual vs predicted and Gains chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plots(agg1,target,type):\n",
    "\n",
    "    plt.figure(1,figsize=(20, 5))\n",
    "\n",
    "    plt.subplot(131)\n",
    "    plt.plot(agg1['DECILE'],agg1['ACTUAL'],label='Actual')\n",
    "    plt.plot(agg1['DECILE'],agg1['PRED'],label='Pred')\n",
    "    plt.xticks(range(10,110,10))\n",
    "    plt.legend(fontsize=15)\n",
    "    plt.grid(True)\n",
    "    plt.title('Actual vs Predicted', fontsize=20)\n",
    "    plt.xlabel(\"Population %\",fontsize=15)\n",
    "    plt.ylabel(str(target) + \" \" + str(type) + \" %\",fontsize=15)\n",
    "\n",
    "    plt.subplot(132)\n",
    "    X = agg1['DECILE'].tolist()\n",
    "    X.append(0)\n",
    "    Y = agg1['DIST_TAR'].tolist()\n",
    "    Y.append(0)\n",
    "    plt.plot(sorted(X),sorted(Y))\n",
    "    plt.plot([0, 100], [0, 100],'r--')\n",
    "    plt.xticks(range(0,110,10))\n",
    "    plt.yticks(range(0,110,10))\n",
    "    plt.grid(True)\n",
    "    plt.title('Gains Chart', fontsize=20)\n",
    "    plt.xlabel(\"Population %\",fontsize=15)\n",
    "    plt.ylabel(str(target) + str(\" DISTRIBUTION\") + \" %\",fontsize=15)\n",
    "    plt.annotate(round(agg1[agg1['DECILE'] == 30].DIST_TAR.item(),2),xy=[30,30], \n",
    "            xytext=(25, agg1[agg1['DECILE'] == 30].DIST_TAR.item() + 5),fontsize = 13)\n",
    "    plt.annotate(round(agg1[agg1['DECILE'] == 50].DIST_TAR.item(),2),xy=[50,50], \n",
    "            xytext=(45, agg1[agg1['DECILE'] == 50].DIST_TAR.item() + 5),fontsize = 13)\n",
    "\n",
    "    plt.subplot(133)\n",
    "    plt.plot(agg1['DECILE'],agg1['LIFT'])\n",
    "    plt.xticks(range(10,110,10))\n",
    "    plt.grid(True)\n",
    "    plt.title('Lift Chart', fontsize=20)\n",
    "    plt.xlabel(\"Population %\",fontsize=15)\n",
    "    plt.ylabel(\"Lift\",fontsize=15)\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gains(data,decile_by,target,score):\n",
    "    inputs = list(decile_by)\n",
    "    inputs.extend((target,score))\n",
    "    decile = data[inputs]\n",
    "    grouped = decile.groupby(decile_by)\n",
    "    agg1 = pd.DataFrame({},index=[])\n",
    "    agg1['ACTUAL'] = grouped.mean()[target]*100\n",
    "    agg1['PRED'] = grouped.mean()[score]*100\n",
    "    agg1['DIST_TAR'] = grouped.sum()[target].cumsum()/grouped.sum()[target].sum()*100\n",
    "    agg1.index.name = 'DECILE'\n",
    "    agg1 = agg1.reset_index()\n",
    "    agg1['DECILE'] = agg1['DECILE']*10\n",
    "    agg1['LIFT'] = agg1['DIST_TAR']/agg1['DECILE']\n",
    "    plots(agg1,target,'Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lift_train = pd.concat([features_train,scores_train],axis=1)\n",
    "lift_test = pd.concat([features_test,scores_test],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nsxhtv37tZF9hHUpJfIKV/L7LHjV9G3CYM6fMOIMxJtsYs9oYM9GuD6cT+OXpV6zYexTj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9fY1hNIAKoBk4HHSyGbsDevAOg7DSq3gRmPw8qvr7mbUoq3etTBw9XCC1M2kS+tMoQQojhqAF4F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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gains(lift_train,['DECILE'],'TARGET','SCORE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YDg04MVkcGuBbNxJ4X3wBR45AYiLcfDPceKM1allVO71bRzDx2nN4aPZaxny2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\nYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gains(lift_test,['DECILE'],'TARGET','SCORE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Model for future use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['final_model.model']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "filename = 'final_model.model'\n",
    "i = [d,clf]\n",
    "joblib.dump(i,filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Score New data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use the code to load the model\n",
    "filename = 'final_model.model'\n",
    "\n",
    "from sklearn.externals import joblib\n",
    "d,clf=joblib.load(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def score_new(features,clf):\n",
    "    score = pd.DataFrame(clf.predict_proba(features)[:,1], columns = ['SCORE'])\n",
    "    score['DECILE'] = pd.qcut(score['SCORE'].rank(method = 'first'),10,labels=range(10,0,-1))\n",
    "    score['DECILE'] = score['DECILE'].astype(float)\n",
    "    return(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'new_score_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-86-8d9b0c82be1b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscore_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_score_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'new_score_data' is not defined"
     ]
    }
   ],
   "source": [
    "scores = score_new(new_score_data,clf)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}