Lecture17_Stacking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# &#50521;&#49345;&#48660;&#46308;&#51032; &#50521;&#49345;&#48660; (&#47784;&#45944; Stacking : Stacked generalization)\n",
    " - &#49436;&#47196; &#45796;&#47480; &#48516;&#47448;&#44592; &#54805;&#53468; &#44036;&#51032; &#50521;&#49345;&#48660;&#46308;&#51032; &#50521;&#49345;&#48660;\n",
    " - &#46041;&#51068; &#54805;&#49885; &#48516;&#47448;&#44592;&#47484; &#49324;&#50857;&#54620; &#48512;&#53944;&#49828;&#53944;&#47017; &#54364;&#48376;&#51012; &#53685;&#54620; &#50521;&#49345;&#48660;&#46308;&#51032; &#50521;&#49345;&#48660;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_columns=None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &#45936;&#51060;&#53552; &#47196;&#46377;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IBM&#50640;&#49436; &#51228;&#44277;&#54664;&#45912; HR &#45936;&#51060;&#53552;&#47484; &#54876;&#50857;&#54616;&#44192;&#49845;&#45768;&#45796;.\n",
    "\n",
    "IBM kaggle &#45936;&#51060;&#53552; : https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>Over18</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>4</td>\n",
       "      <td>Single</td>\n",
       "      <td>5993</td>\n",
       "      <td>19479</td>\n",
       "      <td>8</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>5130</td>\n",
       "      <td>24907</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>2090</td>\n",
       "      <td>2396</td>\n",
       "      <td>6</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>2909</td>\n",
       "      <td>23159</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>3468</td>\n",
       "      <td>16632</td>\n",
       "      <td>9</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
       "0                        2  Female          94               3         2   \n",
       "1                        3    Male          61               2         2   \n",
       "2                        4    Male          92               2         1   \n",
       "3                        4  Female          56               3         1   \n",
       "4                        1    Male          40               3         1   \n",
       "\n",
       "                 JobRole  JobSatisfaction MaritalStatus  MonthlyIncome  \\\n",
       "0        Sales Executive                4        Single           5993   \n",
       "1     Research Scientist                2       Married           5130   \n",
       "2  Laboratory Technician                3        Single           2090   \n",
       "3     Research Scientist                3       Married           2909   \n",
       "4  Laboratory Technician                2       Married           3468   \n",
       "\n",
       "   MonthlyRate  NumCompaniesWorked Over18 OverTime  PercentSalaryHike  \\\n",
       "0        19479                   8      Y      Yes                 11   \n",
       "1        24907                   1      Y       No                 23   \n",
       "2         2396                   6      Y      Yes                 15   \n",
       "3        23159                   1      Y      Yes                 11   \n",
       "4        16632                   9      Y       No                 12   \n",
       "\n",
       "   PerformanceRating  RelationshipSatisfaction  StandardHours  \\\n",
       "0                  3                         1             80   \n",
       "1                  4                         4             80   \n",
       "2                  3                         2             80   \n",
       "3                  3                         3             80   \n",
       "4                  3                         4             80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                 0                  8                      0   \n",
       "1                 1                 10                      3   \n",
       "2                 0                  7                      3   \n",
       "3                 0                  8                      3   \n",
       "4                 1                  6                      3   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                1               6                   4   \n",
       "1                3              10                   7   \n",
       "2                3               0                   0   \n",
       "3                3               8                   7   \n",
       "4                3               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                        0                     5  \n",
       "1                        1                     7  \n",
       "2                        0                     0  \n",
       "3                        3                     0  \n",
       "4                        2                     2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = pd.read_csv('./inputs/HR-Employee-Attrition.csv')\n",
    "datasets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 35)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#45936;&#51060;&#53552;&#47484; &#49332;&#54196;&#48372;&#47732; categorycal features, numerical features&#44032; &#54632;&#44760; &#51080;&#49845;&#45768;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                          int64\n",
       "Attrition                   object\n",
       "BusinessTravel              object\n",
       "DailyRate                    int64\n",
       "Department                  object\n",
       "DistanceFromHome             int64\n",
       "Education                    int64\n",
       "EducationField              object\n",
       "EmployeeCount                int64\n",
       "EmployeeNumber               int64\n",
       "EnvironmentSatisfaction      int64\n",
       "Gender                      object\n",
       "HourlyRate                   int64\n",
       "JobInvolvement               int64\n",
       "JobLevel                     int64\n",
       "JobRole                     object\n",
       "JobSatisfaction              int64\n",
       "MaritalStatus               object\n",
       "MonthlyIncome                int64\n",
       "MonthlyRate                  int64\n",
       "NumCompaniesWorked           int64\n",
       "Over18                      object\n",
       "OverTime                    object\n",
       "PercentSalaryHike            int64\n",
       "PerformanceRating            int64\n",
       "RelationshipSatisfaction     int64\n",
       "StandardHours                int64\n",
       "StockOptionLevel             int64\n",
       "TotalWorkingYears            int64\n",
       "TrainingTimesLastYear        int64\n",
       "WorkLifeBalance              int64\n",
       "YearsAtCompany               int64\n",
       "YearsInCurrentRole           int64\n",
       "YearsSinceLastPromotion      int64\n",
       "YearsWithCurrManager         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taget variable : *Attrition*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes / No -> 1 / 0&#51004;&#47196; &#48320;&#44221;&#54633;&#45768;&#45796;\n",
    "- 1 : &#53748;&#51649; Yes\n",
    "- 0 : &#53748;&#51649; No"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
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       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
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       "      <th>EnvironmentSatisfaction</th>\n",
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       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>Attrition_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
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       "      <td>Female</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>4</td>\n",
       "      <td>Single</td>\n",
       "      <td>5993</td>\n",
       "      <td>19479</td>\n",
       "      <td>8</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
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       "      <td>8</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>5130</td>\n",
       "      <td>24907</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>2090</td>\n",
       "      <td>2396</td>\n",
       "      <td>6</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>2909</td>\n",
       "      <td>23159</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>3468</td>\n",
       "      <td>16632</td>\n",
       "      <td>9</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
       "0                        2  Female          94               3         2   \n",
       "1                        3    Male          61               2         2   \n",
       "2                        4    Male          92               2         1   \n",
       "3                        4  Female          56               3         1   \n",
       "4                        1    Male          40               3         1   \n",
       "\n",
       "                 JobRole  JobSatisfaction MaritalStatus  MonthlyIncome  \\\n",
       "0        Sales Executive                4        Single           5993   \n",
       "1     Research Scientist                2       Married           5130   \n",
       "2  Laboratory Technician                3        Single           2090   \n",
       "3     Research Scientist                3       Married           2909   \n",
       "4  Laboratory Technician                2       Married           3468   \n",
       "\n",
       "   MonthlyRate  NumCompaniesWorked Over18 OverTime  PercentSalaryHike  \\\n",
       "0        19479                   8      Y      Yes                 11   \n",
       "1        24907                   1      Y       No                 23   \n",
       "2         2396                   6      Y      Yes                 15   \n",
       "3        23159                   1      Y      Yes                 11   \n",
       "4        16632                   9      Y       No                 12   \n",
       "\n",
       "   PerformanceRating  RelationshipSatisfaction  StandardHours  \\\n",
       "0                  3                         1             80   \n",
       "1                  4                         4             80   \n",
       "2                  3                         2             80   \n",
       "3                  3                         3             80   \n",
       "4                  3                         4             80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                 0                  8                      0   \n",
       "1                 1                 10                      3   \n",
       "2                 0                  7                      3   \n",
       "3                 0                  8                      3   \n",
       "4                 1                  6                      3   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                1               6                   4   \n",
       "1                3              10                   7   \n",
       "2                3               0                   0   \n",
       "3                3               8                   7   \n",
       "4                3               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  Attrition_idx  \n",
       "0                        0                     5              1  \n",
       "1                        1                     7              0  \n",
       "2                        0                     0              1  \n",
       "3                        3                     0              0  \n",
       "4                        2                     2              0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets['Attrition_idx'] = datasets['Attrition']\\\n",
    "    .apply(lambda x: 1 if x == 'Yes' else 0)\n",
    "datasets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Column &#51204;&#52376;&#47532;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
       "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
       "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
       "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
       "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
       "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
       "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager', 'Attrition_idx'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = datasets.columns\n",
    "col_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#54596;&#50836;&#50630;&#45716; &#48320;&#49688;&#46308;&#51060; &#51080;&#45796; : *EmployeeCount*, *EmployeeNumber*, *Over18*, *StandardHours*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y    1470\n",
      "Name: Over18, dtype: int64\n",
      "1    1470\n",
      "Name: EmployeeCount, dtype: int64\n",
      "80    1470\n",
      "Name: StandardHours, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(datasets.Over18.value_counts())\n",
    "print(datasets.EmployeeCount.value_counts())\n",
    "print(datasets.StandardHours.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Target&#51008; feature&#50640;&#49436; &#51228;&#50808;&#54620;&#45796;.\n",
    "col_names = col_names\\\n",
    "    .drop(['Attrition_idx', 'Attrition', 'Over18', \n",
    "           'EmployeeCount', 'EmployeeNumber', 'StandardHours'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical column&#51012; &#45796;&#47336;&#50612;&#48372;&#51088;.\n",
    "\n",
    "Catagorical column&#51012; numerical column&#51012; &#45208;&#45572;&#50612;&#48372;&#51088;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = []\n",
    "numerical_features = []\n",
    "target = 'Attrition_idx'\n",
    "\n",
    "# feature&#47484; 2&#44032;&#51648; &#54805;&#53468;&#47196; &#44396;&#48516;&#54620;&#45796;.\n",
    "for col in col_names:\n",
    "    if datasets[col].dtype == 'O':\n",
    "        categorical_features.append(col)\n",
    "    else:\n",
    "        numerical_features.append(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical feature&#51032; &#49688; : 7\n",
      "Numerical feature&#51032; &#49688; : 23\n"
     ]
    }
   ],
   "source": [
    "print('Categorical feature&#51032; &#49688; :', len(categorical_features))\n",
    "print('Numerical feature&#51032; &#49688; :', len(numerical_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BusinessTravel',\n",
       " 'Department',\n",
       " 'EducationField',\n",
       " 'Gender',\n",
       " 'JobRole',\n",
       " 'MaritalStatus',\n",
       " 'OverTime']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'DailyRate',\n",
       " 'DistanceFromHome',\n",
       " 'Education',\n",
       " 'EnvironmentSatisfaction',\n",
       " 'HourlyRate',\n",
       " 'JobInvolvement',\n",
       " 'JobLevel',\n",
       " 'JobSatisfaction',\n",
       " 'MonthlyIncome',\n",
       " 'MonthlyRate',\n",
       " 'NumCompaniesWorked',\n",
       " 'PercentSalaryHike',\n",
       " 'PerformanceRating',\n",
       " 'RelationshipSatisfaction',\n",
       " 'StockOptionLevel',\n",
       " 'TotalWorkingYears',\n",
       " 'TrainingTimesLastYear',\n",
       " 'WorkLifeBalance',\n",
       " 'YearsAtCompany',\n",
       " 'YearsInCurrentRole',\n",
       " 'YearsSinceLastPromotion',\n",
       " 'YearsWithCurrManager']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red>Categorical &#45936;&#51060;&#53552;&#47484; one-hot vector&#47196; &#48320;&#44221;</font>&#54616;&#51088;. Pandas&#50640;&#49436; `get_dummies`&#47484; &#51060;&#50857;&#54616;&#51088;.\n",
    "- Train, test set&#51012; &#44396;&#48516;&#54616;&#51648; &#50506;&#44256; &#50896;&#54635;&#48289;&#53552;&#47484; &#47564;&#46300;&#45716; &#44221;&#50864; : &#54644;&#45817; feature&#51032; &#47784;&#46304; &#50896;&#49548;&#46308;&#51012; &#50500;&#45716; &#44221;&#50864;, &#50696;&#47484; &#46308;&#50612; &#54924;&#49324; &#48512;&#49436;, &#44397;&#44032; &#53076;&#46300;&#51032; &#44221;&#50864;&#50640; &#54644;&#45817;&#54620;&#45796;.\n",
    "- Train, test set&#51012; &#44396;&#48516;&#54616;&#44256; train set&#51004;&#47196;&#47564; &#50896;&#54635;&#48289;&#53552;&#47484; &#47564;&#46300;&#45716; &#44221;&#50864; : &#54644;&#45817; feature&#51032; &#50896;&#49548;&#44032; test set&#50640; &#50630;&#45716; &#44221;&#50864;&#44032; &#51316;&#51116;&#54624; &#49688; &#51080;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BusinessTravel_Non-Travel</th>\n",
       "      <th>BusinessTravel_Travel_Frequently</th>\n",
       "      <th>BusinessTravel_Travel_Rarely</th>\n",
       "      <th>Department_Human Resources</th>\n",
       "      <th>Department_Research &amp; Development</th>\n",
       "      <th>Department_Sales</th>\n",
       "      <th>EducationField_Human Resources</th>\n",
       "      <th>EducationField_Life Sciences</th>\n",
       "      <th>EducationField_Marketing</th>\n",
       "      <th>EducationField_Medical</th>\n",
       "      <th>EducationField_Other</th>\n",
       "      <th>EducationField_Technical Degree</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>JobRole_Healthcare Representative</th>\n",
       "      <th>JobRole_Human Resources</th>\n",
       "      <th>JobRole_Laboratory Technician</th>\n",
       "      <th>JobRole_Manager</th>\n",
       "      <th>JobRole_Manufacturing Director</th>\n",
       "      <th>JobRole_Research Director</th>\n",
       "      <th>JobRole_Research Scientist</th>\n",
       "      <th>JobRole_Sales Executive</th>\n",
       "      <th>JobRole_Sales Representative</th>\n",
       "      <th>MaritalStatus_Divorced</th>\n",
       "      <th>MaritalStatus_Married</th>\n",
       "      <th>MaritalStatus_Single</th>\n",
       "      <th>OverTime_No</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "   BusinessTravel_Non-Travel  BusinessTravel_Travel_Frequently  \\\n",
       "0                          0                                 0   \n",
       "1                          0                                 1   \n",
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       "\n",
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       "\n",
       "   EducationField_Marketing  EducationField_Medical  EducationField_Other  \\\n",
       "0                         0                       0                     0   \n",
       "1                         0                       0                     0   \n",
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       "\n",
       "   EducationField_Technical Degree  Gender_Female  Gender_Male  \\\n",
       "0                                0              1            0   \n",
       "1                                0              0            1   \n",
       "2                                0              0            1   \n",
       "3                                0              1            0   \n",
       "4                                0              0            1   \n",
       "\n",
       "   JobRole_Healthcare Representative  JobRole_Human Resources  \\\n",
       "0                                  0                        0   \n",
       "1                                  0                        0   \n",
       "2                                  0                        0   \n",
       "3                                  0                        0   \n",
       "4                                  0                        0   \n",
       "\n",
       "   JobRole_Laboratory Technician  JobRole_Manager  \\\n",
       "0                              0                0   \n",
       "1                              0                0   \n",
       "2                              1                0   \n",
       "3                              0                0   \n",
       "4                              1                0   \n",
       "\n",
       "   JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
       "0                               0                          0   \n",
       "1                               0                          0   \n",
       "2                               0                          0   \n",
       "3                               0                          0   \n",
       "4                               0                          0   \n",
       "\n",
       "   JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                           0                        1   \n",
       "1                           1                        0   \n",
       "2                           0                        0   \n",
       "3                           1                        0   \n",
       "4                           0                        0   \n",
       "\n",
       "   JobRole_Sales Representative  MaritalStatus_Divorced  \\\n",
       "0                             0                       0   \n",
       "1                             0                       0   \n",
       "2                             0                       0   \n",
       "3                             0                       0   \n",
       "4                             0                       0   \n",
       "\n",
       "   MaritalStatus_Married  MaritalStatus_Single  OverTime_No  OverTime_Yes  \n",
       "0                      0                     1            0             1  \n",
       "1                      1                     0            1             0  \n",
       "2                      0                     1            0             1  \n",
       "3                      1                     0            0             1  \n",
       "4                      1                     0            1             0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_datasets = pd.get_dummies(datasets[categorical_features])\n",
    "categorical_datasets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Age  DailyRate  DistanceFromHome  Education  EnvironmentSatisfaction  \\\n",
       "0   41       1102                 1          2                        2   \n",
       "1   49        279                 8          1                        3   \n",
       "2   37       1373                 2          2                        4   \n",
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       "4   27        591                 2          1                        1   \n",
       "\n",
       "   HourlyRate  JobInvolvement  JobLevel  JobSatisfaction  MonthlyIncome  \\\n",
       "0          94               3         2                4           5993   \n",
       "1          61               2         2                2           5130   \n",
       "2          92               2         1                3           2090   \n",
       "3          56               3         1                3           2909   \n",
       "4          40               3         1                2           3468   \n",
       "\n",
       "   MonthlyRate  NumCompaniesWorked  PercentSalaryHike  PerformanceRating  \\\n",
       "0        19479                   8                 11                  3   \n",
       "1        24907                   1                 23                  4   \n",
       "2         2396                   6                 15                  3   \n",
       "3        23159                   1                 11                  3   \n",
       "4        16632                   9                 12                  3   \n",
       "\n",
       "   RelationshipSatisfaction  StockOptionLevel  TotalWorkingYears  \\\n",
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       "1                         4                 1                 10   \n",
       "2                         2                 0                  7   \n",
       "3                         3                 0                  8   \n",
       "4                         4                 1                  6   \n",
       "\n",
       "   TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                      0                1               6                   4   \n",
       "1                      3                3              10                   7   \n",
       "2                      3                3               0                   0   \n",
       "3                      3                3               8                   7   \n",
       "4                      3                3               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                        0                     5  \n",
       "1                        1                     7  \n",
       "2                        0                     0  \n",
       "3                        3                     0  \n",
       "4                        2                     2  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_datasets = datasets[numerical_features]\n",
    "numerical_datasets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical dataset&#44284; numerical dataset&#51012; &#54633;&#52828;&#45796;. &#47784;&#45944;&#51032; input&#51004;&#47196; &#49324;&#50857;&#54624; feature&#51060;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>1373</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2090</td>\n",
       "      <td>2396</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>1392</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2909</td>\n",
       "      <td>23159</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>591</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3468</td>\n",
       "      <td>16632</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BusinessTravel_Non-Travel  BusinessTravel_Travel_Frequently  \\\n",
       "0                          0                                 0   \n",
       "1                          0                                 1   \n",
       "2                          0                                 0   \n",
       "3                          0                                 1   \n",
       "4                          0                                 0   \n",
       "\n",
       "   BusinessTravel_Travel_Rarely  Department_Human Resources  \\\n",
       "0                             1                           0   \n",
       "1                             0                           0   \n",
       "2                             1                           0   \n",
       "3                             0                           0   \n",
       "4                             1                           0   \n",
       "\n",
       "   Department_Research & Development  Department_Sales  \\\n",
       "0                                  0                 1   \n",
       "1                                  1                 0   \n",
       "2                                  1                 0   \n",
       "3                                  1                 0   \n",
       "4                                  1                 0   \n",
       "\n",
       "   EducationField_Human Resources  EducationField_Life Sciences  \\\n",
       "0                               0                             1   \n",
       "1                               0                             1   \n",
       "2                               0                             0   \n",
       "3                               0                             1   \n",
       "4                               0                             0   \n",
       "\n",
       "   EducationField_Marketing  EducationField_Medical  EducationField_Other  \\\n",
       "0                         0                       0                     0   \n",
       "1                         0                       0                     0   \n",
       "2                         0                       0                     1   \n",
       "3                         0                       0                     0   \n",
       "4                         0                       1                     0   \n",
       "\n",
       "   EducationField_Technical Degree  Gender_Female  Gender_Male  \\\n",
       "0                                0              1            0   \n",
       "1                                0              0            1   \n",
       "2                                0              0            1   \n",
       "3                                0              1            0   \n",
       "4                                0              0            1   \n",
       "\n",
       "   JobRole_Healthcare Representative  JobRole_Human Resources  \\\n",
       "0                                  0                        0   \n",
       "1                                  0                        0   \n",
       "2                                  0                        0   \n",
       "3                                  0                        0   \n",
       "4                                  0                        0   \n",
       "\n",
       "   JobRole_Laboratory Technician  JobRole_Manager  \\\n",
       "0                              0                0   \n",
       "1                              0                0   \n",
       "2                              1                0   \n",
       "3                              0                0   \n",
       "4                              1                0   \n",
       "\n",
       "   JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
       "0                               0                          0   \n",
       "1                               0                          0   \n",
       "2                               0                          0   \n",
       "3                               0                          0   \n",
       "4                               0                          0   \n",
       "\n",
       "   JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                           0                        1   \n",
       "1                           1                        0   \n",
       "2                           0                        0   \n",
       "3                           1                        0   \n",
       "4                           0                        0   \n",
       "\n",
       "   JobRole_Sales Representative  MaritalStatus_Divorced  \\\n",
       "0                             0                       0   \n",
       "1                             0                       0   \n",
       "2                             0                       0   \n",
       "3                             0                       0   \n",
       "4                             0                       0   \n",
       "\n",
       "   MaritalStatus_Married  MaritalStatus_Single  OverTime_No  OverTime_Yes  \\\n",
       "0                      0                     1            0             1   \n",
       "1                      1                     0            1             0   \n",
       "2                      0                     1            0             1   \n",
       "3                      1                     0            0             1   \n",
       "4                      1                     0            1             0   \n",
       "\n",
       "   Age  DailyRate  DistanceFromHome  Education  EnvironmentSatisfaction  \\\n",
       "0   41       1102                 1          2                        2   \n",
       "1   49        279                 8          1                        3   \n",
       "2   37       1373                 2          2                        4   \n",
       "3   33       1392                 3          4                        4   \n",
       "4   27        591                 2          1                        1   \n",
       "\n",
       "   HourlyRate  JobInvolvement  JobLevel  JobSatisfaction  MonthlyIncome  \\\n",
       "0          94               3         2                4           5993   \n",
       "1          61               2         2                2           5130   \n",
       "2          92               2         1                3           2090   \n",
       "3          56               3         1                3           2909   \n",
       "4          40               3         1                2           3468   \n",
       "\n",
       "   MonthlyRate  NumCompaniesWorked  PercentSalaryHike  PerformanceRating  \\\n",
       "0        19479                   8                 11                  3   \n",
       "1        24907                   1                 23                  4   \n",
       "2         2396                   6                 15                  3   \n",
       "3        23159                   1                 11                  3   \n",
       "4        16632                   9                 12                  3   \n",
       "\n",
       "   RelationshipSatisfaction  StockOptionLevel  TotalWorkingYears  \\\n",
       "0                         1                 0                  8   \n",
       "1                         4                 1                 10   \n",
       "2                         2                 0                  7   \n",
       "3                         3                 0                  8   \n",
       "4                         4                 1                  6   \n",
       "\n",
       "   TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                      0                1               6                   4   \n",
       "1                      3                3              10                   7   \n",
       "2                      3                3               0                   0   \n",
       "3                      3                3               8                   7   \n",
       "4                      3                3               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                        0                     5  \n",
       "1                        1                     7  \n",
       "2                        0                     0  \n",
       "3                        3                     0  \n",
       "4                        2                     2  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.concat([categorical_datasets, numerical_datasets], axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    0\n",
       "Name: Attrition_idx, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = datasets[target]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train set&#44284; test set&#51012; &#44396;&#48516;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = \\\n",
    "    train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &#47784;&#45944;&#51032; &#49457;&#45733;&#51012; &#54217;&#44032;&#54616;&#44592; &#50948;&#54620; metric&#46308;&#51012; &#51221;&#51032;&#54620; helper &#54632;&#49688; &#51221;&#51032;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Help &#54632;&#49688;\n",
    "def get_metric(y_label, pred, set_type):\n",
    "    # 1. Confusion Matrix\n",
    "    print('\\n {} Confusion Matrix :'.format(set_type))\n",
    "    display(pd.crosstab(y_label, pred, rownames=['Actual'], colnames=['Predict']))\n",
    "\n",
    "    # 2. Accuracy\n",
    "    print('\\n {} accuracy :'.format(set_type), accuracy_score(y_label, pred))\n",
    "\n",
    "    # 3. Classification Report\n",
    "    print('\\n {} Classification Report : \\n'.format(set_type), classification_report(y_label, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=red>Out-of-fold&#47484; &#49324;&#50857;&#54620; Stacking&#51012; &#50948;&#54620; helper &#54632;&#49688; &#51221;&#51032;</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "splitter = StratifiedKFold(n_splits=5, shuffle=False, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross validation&#51032; &#44600;&#51060; 5\n"
     ]
    }
   ],
   "source": [
    "print('cross validation&#51032; &#44600;&#51060;', len(list(splitter.split(x_train, y_train))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 199,  200,  201,  202,  203,  205,  206,  207,  208,  209,  210,\n",
       "         211,  212,  213,  214,  215,  216,  217,  219,  220,  221,  222,\n",
       "         223,  225,  226,  227,  229,  230,  231,  232,  233,  234,  236,\n",
       "         237,  238,  239,  240,  241,  242,  244,  245,  247,  249,  250,\n",
       "         251,  252,  253,  254,  255,  256,  257,  258,  259,  260,  261,\n",
       "         262,  263,  264,  265,  266,  267,  268,  269,  270,  271,  272,\n",
       "         273,  274,  275,  276,  277,  278,  279,  280,  281,  282,  283,\n",
       "         284,  285,  286,  287,  288,  289,  290,  291,  292,  293,  294,\n",
       "         295,  296,  297,  298,  299,  300,  301,  302,  303,  304,  305,\n",
       "         306,  307,  308,  309,  310,  311,  312,  313,  314,  315,  316,\n",
       "         317,  318,  319,  320,  321,  322,  323,  324,  325,  326,  327,\n",
       "         328,  329,  330,  331,  332,  333,  334,  335,  336,  337,  338,\n",
       "         339,  340,  341,  342,  343,  344,  345,  346,  347,  348,  349,\n",
       "         350,  351,  352,  353,  354,  355,  356,  357,  358,  359,  360,\n",
       "         361,  362,  363,  364,  365,  366,  367,  368,  369,  370,  371,\n",
       "         372,  373,  374,  375,  376,  377,  378,  379,  380,  381,  382,\n",
       "         383,  384,  385,  386,  387,  388,  389,  390,  391,  392,  393,\n",
       "         394,  395,  396,  397,  398,  399,  400,  401,  402,  403,  404,\n",
       "         405,  406,  407,  408,  409,  410,  411,  412,  413,  414,  415,\n",
       "         416,  417,  418,  419,  420,  421,  422,  423,  424,  425,  426,\n",
       "         427,  428,  429,  430,  431,  432,  433,  434,  435,  436,  437,\n",
       "         438,  439,  440,  441,  442,  443,  444,  445,  446,  447,  448,\n",
       "         449,  450,  451,  452,  453,  454,  455,  456,  457,  458,  459,\n",
       "         460,  461,  462,  463,  464,  465,  466,  467,  468,  469,  470,\n",
       "         471,  472,  473,  474,  475,  476,  477,  478,  479,  480,  481,\n",
       "         482,  483,  484,  485,  486,  487,  488,  489,  490,  491,  492,\n",
       "         493,  494,  495,  496,  497,  498,  499,  500,  501,  502,  503,\n",
       "         504,  505,  506,  507,  508,  509,  510,  511,  512,  513,  514,\n",
       "         515,  516,  517,  518,  519,  520,  521,  522,  523,  524,  525,\n",
       "         526,  527,  528,  529,  530,  531,  532,  533,  534,  535,  536,\n",
       "         537,  538,  539,  540,  541,  542,  543,  544,  545,  546,  547,\n",
       "         548,  549,  550,  551,  552,  553,  554,  555,  556,  557,  558,\n",
       "         559,  560,  561,  562,  563,  564,  565,  566,  567,  568,  569,\n",
       "         570,  571,  572,  573,  574,  575,  576,  577,  578,  579,  580,\n",
       "         581,  582,  583,  584,  585,  586,  587,  588,  589,  590,  591,\n",
       "         592,  593,  594,  595,  596,  597,  598,  599,  600,  601,  602,\n",
       "         603,  604,  605,  606,  607,  608,  609,  610,  611,  612,  613,\n",
       "         614,  615,  616,  617,  618,  619,  620,  621,  622,  623,  624,\n",
       "         625,  626,  627,  628,  629,  630,  631,  632,  633,  634,  635,\n",
       "         636,  637,  638,  639,  640,  641,  642,  643,  644,  645,  646,\n",
       "         647,  648,  649,  650,  651,  652,  653,  654,  655,  656,  657,\n",
       "         658,  659,  660,  661,  662,  663,  664,  665,  666,  667,  668,\n",
       "         669,  670,  671,  672,  673,  674,  675,  676,  677,  678,  679,\n",
       "         680,  681,  682,  683,  684,  685,  686,  687,  688,  689,  690,\n",
       "         691,  692,  693,  694,  695,  696,  697,  698,  699,  700,  701,\n",
       "         702,  703,  704,  705,  706,  707,  708,  709,  710,  711,  712,\n",
       "         713,  714,  715,  716,  717,  718,  719,  720,  721,  722,  723,\n",
       "         724,  725,  726,  727,  728,  729,  730,  731,  732,  733,  734,\n",
       "         735,  736,  737,  738,  739,  740,  741,  742,  743,  744,  745,\n",
       "         746,  747,  748,  749,  750,  751,  752,  753,  754,  755,  756,\n",
       "         757,  758,  759,  760,  761,  762,  763,  764,  765,  766,  767,\n",
       "         768,  769,  770,  771,  772,  773,  774,  775,  776,  777,  778,\n",
       "         779,  780,  781,  782,  783,  784,  785,  786,  787,  788,  789,\n",
       "         790,  791,  792,  793,  794,  795,  796,  797,  798,  799,  800,\n",
       "         801,  802,  803,  804,  805,  806,  807,  808,  809,  810,  811,\n",
       "         812,  813,  814,  815,  816,  817,  818,  819,  820,  821,  822,\n",
       "         823,  824,  825,  826,  827,  828,  829,  830,  831,  832,  833,\n",
       "         834,  835,  836,  837,  838,  839,  840,  841,  842,  843,  844,\n",
       "         845,  846,  847,  848,  849,  850,  851,  852,  853,  854,  855,\n",
       "         856,  857,  858,  859,  860,  861,  862,  863,  864,  865,  866,\n",
       "         867,  868,  869,  870,  871,  872,  873,  874,  875,  876,  877,\n",
       "         878,  879,  880,  881,  882,  883,  884,  885,  886,  887,  888,\n",
       "         889,  890,  891,  892,  893,  894,  895,  896,  897,  898,  899,\n",
       "         900,  901,  902,  903,  904,  905,  906,  907,  908,  909,  910,\n",
       "         911,  912,  913,  914,  915,  916,  917,  918,  919,  920,  921,\n",
       "         922,  923,  924,  925,  926,  927,  928,  929,  930,  931,  932,\n",
       "         933,  934,  935,  936,  937,  938,  939,  940,  941,  942,  943,\n",
       "         944,  945,  946,  947,  948,  949,  950,  951,  952,  953,  954,\n",
       "         955,  956,  957,  958,  959,  960,  961,  962,  963,  964,  965,\n",
       "         966,  967,  968,  969,  970,  971,  972,  973,  974,  975,  976,\n",
       "         977,  978,  979,  980,  981,  982,  983,  984,  985,  986,  987,\n",
       "         988,  989,  990,  991,  992,  993,  994,  995,  996,  997,  998,\n",
       "         999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009,\n",
       "        1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020,\n",
       "        1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028]),\n",
       " array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "         13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "         26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "         39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "         52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "         65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "         78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "         91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "        104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "        117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "        130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "        143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "        156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "        169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "        182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
       "        195, 196, 197, 198, 204, 218, 224, 228, 235, 243, 246, 248]))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cv&#50640;&#49436; &#49692;&#49436;&#44032; &#48372;&#51109;&#46104;&#45716;&#51648; &#54869;&#51064;!\n",
    "list(splitter.split(x_train, y_train))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Out-of-fold\n",
    "def get_oof(classifier, x_train, y_train, x_test):\n",
    "    oof_val = []\n",
    "    oof_test = pd.DataFrame()\n",
    "    clf_name = classifier.__class__.__name__\n",
    "    \n",
    "    for i, (train_indice, test_indice) in enumerate(splitter.split(x_train, y_train)):\n",
    "        x_cv_train = x_train.iloc[train_indice]\n",
    "        y_cv_train = y_train.iloc[train_indice]\n",
    "        x_cv_val = x_train.iloc[test_indice]\n",
    "        \n",
    "        # 4&#44060;&#51032; fold&#47196; &#54617;&#49845; (&#52395; &#48264;&#51704; layer &#54617;&#49845;)\n",
    "        classifier.fit(x_cv_train, y_cv_train)\n",
    "        # 1&#44060;&#51032; fold&#47196; &#50696;&#52769; (&#46160; &#48264;&#51704; layer&#51032; input)\n",
    "        oof_val.extend(classifier.predict_proba(x_cv_val)[:,1])\n",
    "        # &#52572;&#51333; &#49457;&#45733; &#52769;&#51221;&#51012; &#50948;&#54644;&#49436; &#44033; cross validation classifier &#48324;&#47196; &#50696;&#52769;&#44050;&#51012; &#47784;&#51008;&#45796;.\n",
    "        oof_test[i] = classifier.predict_proba(x_test)[:,1]\n",
    "        \n",
    "    oof_train = pd.DataFrame({clf_name: oof_val})\n",
    "    oof_test[clf_name + '_mean'] = oof_test.mean(axis=1)\n",
    "    \n",
    "    return oof_train, oof_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## &#49436;&#47196; &#45796;&#47480; &#48516;&#47448;&#44592; &#54805;&#53468; &#44036;&#51032; &#50521;&#49345;&#48660;&#46308;&#51032; &#50521;&#49345;&#48660;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- &#54984;&#47144; &#45936;&#51060;&#53552;&#50640; &#44033; &#45348; &#44032;&#51648; &#48516;&#47448;&#44592;&#47484; &#48324;&#46020;&#47196; &#51201;&#50857;(&#47196;&#51648;&#49828;&#54001; &#54924;&#44480;, &#51032;&#49324;&#44208;&#51221; &#53944;&#47532;, &#47016;&#45924; &#54252;&#47112;&#49828;&#53944;, &#50640;&#51060;&#45796; &#48512;&#49828;&#53944;)\n",
    "- &#45348; &#44032;&#51648; &#48516;&#47448;&#44592;&#50640; &#44288;&#54620; &#54869;&#47456;&#51012; &#44228;&#49328;&#54620;&#45796;. &#44536;&#47532;&#44256; &#48512;&#47448; 1&#50640; &#44288;&#54620; &#54869;&#47456;&#47564; &#47700;&#53440; &#48516;&#47448;&#44592;&#50640; &#51060;&#50857;&#54620;&#45796;. &#48512;&#47448; 0&#51032; &#54869;&#47456; + &#48512;&#47448; 1&#51032; &#54869;&#47456; = 1&#51060;&#48064;&#47196; &#54616;&#45208;&#51032; &#54869;&#47456;&#47564; &#44228;&#49328;&#54644;&#46020; &#52649;&#48516;&#54616;&#45796;. &#44536;&#47111;&#51648; &#50506;&#51004;&#47732; &#45796;&#51473;&#44277;&#49440;&#49457; &#47928;&#51228;&#44032; &#48156;&#49373;&#54620;&#45796;.\n",
    "- &#52572;&#51333; 0/1 &#52636;&#47141;&#50640; &#44288;&#54620; 4&#44060;&#51032; &#54869;&#47456;(&#44033; &#48516;&#47448;&#44592;&#50640;&#49436; &#50619;&#51008; &#44050;) &#44036;&#51032; &#44288;&#44228;&#47484; &#47784;&#45944;&#47553;&#54616;&#44592; &#50948;&#54644; &#47196;&#51648;&#49828;&#54001; &#54924;&#44480;&#47484; &#47700;&#53440; &#48516;&#47448;&#44592;&#47196; &#49324;&#50857;&#54664;&#45796;.\n",
    "- &#47700;&#53440; &#48516;&#47448;&#44592;&#50640; &#49324;&#50857;&#46108; 4&#44060; &#48320;&#49688; &#47784;&#46160;&#50640; &#44288;&#54620; &#44228;&#49688;&#47484; &#44228;&#49328;&#54616;&#44256; &#49352;&#47196;&#50868; &#45936;&#51060;&#53552;&#50640; &#51201;&#50857;&#54644; &#44288;&#52769;&#44050;&#51012; &#52572;&#51333; &#48512;&#47448;&#47196; &#48516;&#47448;&#54616;&#44592; &#50948;&#54620; &#44228;&#49328;&#51012; &#54620;&#45796;."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. &#48516;&#47448;&#44592;&#50640; &#45824;&#54616;&#50668; &#54617;&#49845;&#51012; &#51652;&#54665; (Out_of_fold &#44592;&#48277;&#51012; &#54876;&#50857;&#54616;&#50668;)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decision tree &#44053;&#51032;&#50640;&#49436; &#53364;&#47000;&#49828; &#44032;&#51473;&#52824;&#47484; &#44396;&#54664;&#49845;&#45768;&#45796;.\n",
    "c_weight = {0: 0.3, 1: 0.7}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> &#47196;&#51648;&#49828;&#54001; &#54924;&#44480; &#48516;&#47448;&#44592; out-of-fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr_classifier = LogisticRegression(fit_intercept=True, class_weight=c_weight, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_train_oof_pred, lr_test_oof_pred = get_oof(lr_classifier, x_train, y_train, x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1029\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>LogisticRegression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.173167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.737308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.436940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.138797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LogisticRegression\n",
       "0            0.002691\n",
       "1            0.173167\n",
       "2            0.737308\n",
       "3            0.436940\n",
       "4            0.138797"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(lr_train_oof_pred))\n",
    "lr_train_oof_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "441\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>LogisticRegression_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.162808</td>\n",
       "      <td>0.183174</td>\n",
       "      <td>0.116924</td>\n",
       "      <td>0.176768</td>\n",
       "      <td>0.172023</td>\n",
       "      <td>0.162339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.027554</td>\n",
       "      <td>0.028848</td>\n",
       "      <td>0.023524</td>\n",
       "      <td>0.043728</td>\n",
       "      <td>0.037672</td>\n",
       "      <td>0.032265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.648121</td>\n",
       "      <td>0.343309</td>\n",
       "      <td>0.472608</td>\n",
       "      <td>0.373633</td>\n",
       "      <td>0.451662</td>\n",
       "      <td>0.457867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.008330</td>\n",
       "      <td>0.009205</td>\n",
       "      <td>0.010312</td>\n",
       "      <td>0.027672</td>\n",
       "      <td>0.014404</td>\n",
       "      <td>0.013984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.121680</td>\n",
       "      <td>0.141694</td>\n",
       "      <td>0.142263</td>\n",
       "      <td>0.083988</td>\n",
       "      <td>0.139296</td>\n",
       "      <td>0.125784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4  LogisticRegression_mean\n",
       "0  0.162808  0.183174  0.116924  0.176768  0.172023                 0.162339\n",
       "1  0.027554  0.028848  0.023524  0.043728  0.037672                 0.032265\n",
       "2  0.648121  0.343309  0.472608  0.373633  0.451662                 0.457867\n",
       "3  0.008330  0.009205  0.010312  0.027672  0.014404                 0.013984\n",
       "4  0.121680  0.141694  0.142263  0.083988  0.139296                 0.125784"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(lr_test_oof_pred))\n",
    "lr_test_oof_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train Confusion Matrix :\n"
     ]
    },
    {
     "data": {
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       "<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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       "    }\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>Predict</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>779</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0    1\n",
       "Actual           \n",
       "0        779   74\n",
       "1         62  114"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train accuracy : 0.8678328474246841\n",
      "\n",
      " Train Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.91      0.92       853\n",
      "          1       0.61      0.65      0.63       176\n",
      "\n",
      "avg / total       0.87      0.87      0.87      1029\n",
      "\n",
      "============================================================\n",
      "\n",
      " Test Confusion Matrix :\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<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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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>0</th>\n",
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       "    </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>339</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0   1\n",
       "Actual          \n",
       "0        339  41\n",
       "1         31  30"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy : 0.8367346938775511\n",
      "\n",
      " Test Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.89      0.90       380\n",
      "          1       0.42      0.49      0.45        61\n",
      "\n",
      "avg / total       0.85      0.84      0.84       441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# &#47196;&#51648;&#49828;&#54001; &#54924;&#44480; &#48516;&#47448;&#44592; &#47784;&#45944;&#47564; &#51060;&#50857;&#54616;&#50668; &#50696;&#52769;&#44050;&#51012; &#44396;&#54620;&#45796;.\n",
    "lr_classifier.fit(x_train, y_train)\n",
    "train_pred = lr_classifier.predict(x_train)\n",
    "test_pred = lr_classifier.predict(x_test)\n",
    "\n",
    "# &#47196;&#51648;&#49828;&#54001; &#54924;&#44480; &#48516;&#47448;&#44592; &#47784;&#45944;&#51032; &#49457;&#45733;&#51012; &#44396;&#54620;&#45796;.\n",
    "get_metric(y_train, train_pred, 'Train')\n",
    "print('=' * 60)\n",
    "get_metric(y_test, test_pred, 'Test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> &#44208;&#51221; &#53944;&#47532; &#48516;&#47448;&#44592; out-of-fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt_classifier = DecisionTreeClassifier(\n",
    "    max_depth=5,\n",
    "    min_samples_split=2,\n",
    "    min_samples_leaf=1,\n",
    "    class_weight=c_weight,\n",
    "    random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_train_oof_pred, dt_test_oof_pred = get_oof(dt_classifier, x_train, y_train, x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1029\n"
     ]
    },
    {
     "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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       "    }\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>DecisionTreeClassifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.049296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.252351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.903226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.049296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.475728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DecisionTreeClassifier\n",
       "0                0.049296\n",
       "1                0.252351\n",
       "2                0.903226\n",
       "3                0.049296\n",
       "4                0.475728"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(dt_train_oof_pred))\n",
    "dt_train_oof_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "441\n"
     ]
    },
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>DecisionTreeClassifier_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.252351</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.088129</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.126429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.252351</td>\n",
       "      <td>0.089921</td>\n",
       "      <td>0.088129</td>\n",
       "      <td>0.072165</td>\n",
       "      <td>0.062710</td>\n",
       "      <td>0.113055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.823529</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.761797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.089921</td>\n",
       "      <td>0.088129</td>\n",
       "      <td>0.047490</td>\n",
       "      <td>0.062710</td>\n",
       "      <td>0.057650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.014463</td>\n",
       "      <td>0.091205</td>\n",
       "      <td>0.237288</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.168591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4  \\\n",
       "0  0.252351  0.291667  0.088129  0.000000  0.000000   \n",
       "1  0.252351  0.089921  0.088129  0.072165  0.062710   \n",
       "2  0.823529  1.000000  0.350000  0.840000  0.795455   \n",
       "3  0.000000  0.089921  0.088129  0.047490  0.062710   \n",
       "4  0.014463  0.091205  0.237288  0.500000  0.000000   \n",
       "\n",
       "   DecisionTreeClassifier_mean  \n",
       "0                     0.126429  \n",
       "1                     0.113055  \n",
       "2                     0.761797  \n",
       "3                     0.057650  \n",
       "4                     0.168591  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(dt_test_oof_pred))\n",
    "dt_test_oof_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train Confusion Matrix :\n"
     ]
    },
    {
     "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>Predict</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>787</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0    1\n",
       "Actual           \n",
       "0        787   66\n",
       "1         56  120"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train accuracy : 0.8814382896015549\n",
      "\n",
      " Train Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.92      0.93       853\n",
      "          1       0.65      0.68      0.66       176\n",
      "\n",
      "avg / total       0.88      0.88      0.88      1029\n",
      "\n",
      "============================================================\n",
      "\n",
      " Test Confusion Matrix :\n"
     ]
    },
    {
     "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>Predict</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>345</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0   1\n",
       "Actual          \n",
       "0        345  35\n",
       "1         36  25"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy : 0.8390022675736961\n",
      "\n",
      " Test Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.91      0.91       380\n",
      "          1       0.42      0.41      0.41        61\n",
      "\n",
      "avg / total       0.84      0.84      0.84       441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# &#44208;&#51221; &#53944;&#47532; &#48516;&#47448;&#44592; &#47784;&#45944;&#47564;&#51012; &#51060;&#50857;&#54616;&#50668; &#50696;&#52769;&#44050;&#51012; &#49373;&#49457;&#54620;&#45796;.\n",
    "dt_classifier.fit(x_train, y_train)\n",
    "train_pred = dt_classifier.predict(x_train)\n",
    "test_pred = dt_classifier.predict(x_test)\n",
    "\n",
    "# &#44208;&#51221; &#53944;&#47532; &#48516;&#47448;&#44592; &#47784;&#45944;&#51032; &#49457;&#45733;&#51012; &#54217;&#44032;&#54644;&#48372;&#51088;.\n",
    "get_metric(y_train, train_pred, 'Train')\n",
    "print('=' * 60)\n",
    "get_metric(y_test, test_pred, 'Test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> &#47004;&#45924; &#54252;&#47112;&#49828;&#53944; &#48516;&#47448;&#44592; out-of-fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_classifier = RandomForestClassifier(\n",
    "    n_estimators=10000,\n",
    "    max_depth=6,\n",
    "    min_samples_split=2,\n",
    "    min_samples_leaf=1,\n",
    "    class_weight=c_weight,\n",
    "    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_train_oof_pred, rf_test_oof_pred = get_oof(rf_classifier, x_train, y_train, x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train Confusion Matrix :\n"
     ]
    },
    {
     "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>Predict</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>846</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0    1\n",
       "Actual           \n",
       "0        846    7\n",
       "1         61  115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train accuracy : 0.9339164237123421\n",
      "\n",
      " Train Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.99      0.96       853\n",
      "          1       0.94      0.65      0.77       176\n",
      "\n",
      "avg / total       0.93      0.93      0.93      1029\n",
      "\n",
      "============================================================\n",
      "\n",
      " Test Confusion Matrix :\n"
     ]
    },
    {
     "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>Predict</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>373</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0  1\n",
       "Actual         \n",
       "0        373  7\n",
       "1         53  8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy : 0.8639455782312925\n",
      "\n",
      " Test Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.98      0.93       380\n",
      "          1       0.53      0.13      0.21        61\n",
      "\n",
      "avg / total       0.83      0.86      0.83       441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# &#47004;&#45924; &#54252;&#47112;&#49828;&#53944; &#48516;&#47448;&#44592;&#47564;&#51012; &#51060;&#50857;&#54616;&#50668; &#50696;&#52769;&#44050;&#51012; &#44396;&#54633;&#45768;&#45796;.\n",
    "rf_classifier.fit(x_train, y_train)\n",
    "train_pred = rf_classifier.predict(x_train)\n",
    "test_pred = rf_classifier.predict(x_test)\n",
    "\n",
    "# &#47004;&#45924; &#54252;&#47112;&#49828;&#53944; &#48516;&#47448;&#44592;&#51032; &#49457;&#45733;&#51012; &#52769;&#51221;&#54633;&#45768;&#45796;.\n",
    "get_metric(y_train, train_pred, 'Train')\n",
    "print('=' * 60)\n",
    "get_metric(y_test, test_pred, 'Test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> &#50640;&#51060;&#45796; &#48512;&#49828;&#53944; &#48516;&#47448;&#44592;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ab_classifier = AdaBoostClassifier(\n",
    "    DecisionTreeClassifier(max_depth=1, class_weight=c_weight),\n",
    "    n_estimators=5000,\n",
    "    learning_rate=0.05,\n",
    "    random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ab_train_oof_pred, ab_test_oof_pred = get_oof(ab_classifier, x_train, y_train, x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train Confusion Matrix :\n"
     ]
    },
    {
     "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>Predict</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>809</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0    1\n",
       "Actual           \n",
       "0        809   44\n",
       "1         25  151"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train accuracy : 0.9329446064139941\n",
      "\n",
      " Train Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      0.95      0.96       853\n",
      "          1       0.77      0.86      0.81       176\n",
      "\n",
      "avg / total       0.94      0.93      0.93      1029\n",
      "\n",
      "============================================================\n",
      "\n",
      " Test Confusion Matrix :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Predict</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>342</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0   1\n",
       "Actual          \n",
       "0        342  38\n",
       "1         33  28"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy : 0.8390022675736961\n",
      "\n",
      " Test Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.90      0.91       380\n",
      "          1       0.42      0.46      0.44        61\n",
      "\n",
      "avg / total       0.84      0.84      0.84       441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# &#50640;&#51060;&#45796; &#48512;&#49828;&#53944; &#48516;&#47448;&#44592;&#47564;&#51012; &#51060;&#50857;&#54616;&#50668; &#50696;&#52769;&#44050;&#51012; &#49373;&#49457;&#54633;&#45768;&#45796;.\n",
    "ab_classifier.fit(x_train, y_train)\n",
    "train_pred = ab_classifier.predict(x_train)\n",
    "test_pred = ab_classifier.predict(x_test)\n",
    "\n",
    "# &#50640;&#51060;&#45796; &#48512;&#49828;&#53944; &#48516;&#47448;&#44592;&#51032; &#49457;&#45733;&#51012; &#52769;&#51221;&#54633;&#45768;&#45796;.\n",
    "get_metric(y_train, train_pred, 'Train')\n",
    "print('=' * 60)\n",
    "get_metric(y_test, test_pred, 'Test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. &#48516;&#47448;&#44592;&#51032; &#44208;&#44284;&#46308;&#51012; &#44208;&#54633;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensemble = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#48512;&#47448; 1(&#53748;&#51649;&#51088;)&#50640; &#44288;&#54620; &#54869;&#47456;&#51012; &#51060;&#50857;&#54616;&#50668; &#50521;&#49345;&#48660;&#51012; &#49688;&#54665;&#54633;&#45768;&#45796;."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> &#47196;&#51648;&#49828;&#54001; &#54924;&#44480; &#48516;&#47448;&#44592;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>lr_output_one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.173167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.737308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.436940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.138797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lr_output_one\n",
       "0       0.002691\n",
       "1       0.173167\n",
       "2       0.737308\n",
       "3       0.436940\n",
       "4       0.138797"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#47196;&#51648;&#49828;&#54001; &#54924;&#44480; &#48516;&#47448;&#44592;&#50640; &#45824;&#54620; &#44208;&#44284;&#44050;&#51012;, &#48512;&#47448; 1&#51060; &#46112; &#54869;&#47456;&#47564; &#47532;&#53556;&#48155;&#49845;&#45768;&#45796;.\n",
    "ensemble['lr_output_one'] = lr_train_oof_pred\n",
    "ensemble.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> &#44208;&#51221; &#53944;&#47532; &#48516;&#47448;&#44592; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>lr_output_one</th>\n",
       "      <th>dt_output_one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002691</td>\n",
       "      <td>0.049296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.173167</td>\n",
       "      <td>0.252351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.737308</td>\n",
       "      <td>0.903226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.436940</td>\n",
       "      <td>0.049296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.138797</td>\n",
       "      <td>0.475728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lr_output_one  dt_output_one\n",
       "0       0.002691       0.049296\n",
       "1       0.173167       0.252351\n",
       "2       0.737308       0.903226\n",
       "3       0.436940       0.049296\n",
       "4       0.138797       0.475728"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#44208;&#51221; &#53944;&#47532; &#48516;&#47448;&#44592;&#50640; &#45824;&#54620; &#44208;&#44284;&#44050;&#51012;, &#48512;&#47448; 1&#51060; &#46112; &#54869;&#47456;&#47564; &#47532;&#53556;&#48155;&#49845;&#45768;&#45796;.\n",
    "ensemble['dt_output_one'] = dt_train_oof_pred\n",
    "ensemble.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> &#47004;&#45924; &#54252;&#47112;&#49828;&#53944; &#48516;&#47448;&#44592;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>lr_output_one</th>\n",
       "      <th>dt_output_one</th>\n",
       "      <th>rf_output_one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002691</td>\n",
       "      <td>0.049296</td>\n",
       "      <td>0.079142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.173167</td>\n",
       "      <td>0.252351</td>\n",
       "      <td>0.128049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.737308</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.529802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.436940</td>\n",
       "      <td>0.049296</td>\n",
       "      <td>0.125879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.138797</td>\n",
       "      <td>0.475728</td>\n",
       "      <td>0.119156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lr_output_one  dt_output_one  rf_output_one\n",
       "0       0.002691       0.049296       0.079142\n",
       "1       0.173167       0.252351       0.128049\n",
       "2       0.737308       0.903226       0.529802\n",
       "3       0.436940       0.049296       0.125879\n",
       "4       0.138797       0.475728       0.119156"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#47004;&#45924; &#54252;&#47112;&#49828;&#53944; &#48516;&#47448;&#44592;&#50640; &#45824;&#54620; &#44208;&#44284;&#44050;&#51012;, &#48512;&#47448; 1&#51060; &#46112; &#54869;&#47456;&#47564; &#47532;&#53556;&#48155;&#49845;&#45768;&#45796;.\n",
    "ensemble['rf_output_one'] = rf_train_oof_pred\n",
    "ensemble.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> &#50640;&#51060;&#45796; &#48512;&#49828;&#53944; &#48516;&#47448;&#44592;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>lr_output_one</th>\n",
       "      <th>dt_output_one</th>\n",
       "      <th>rf_output_one</th>\n",
       "      <th>ab_output_one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002691</td>\n",
       "      <td>0.049296</td>\n",
       "      <td>0.079142</td>\n",
       "      <td>0.487058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.173167</td>\n",
       "      <td>0.252351</td>\n",
       "      <td>0.128049</td>\n",
       "      <td>0.490910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.737308</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.529802</td>\n",
       "      <td>0.500735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.436940</td>\n",
       "      <td>0.049296</td>\n",
       "      <td>0.125879</td>\n",
       "      <td>0.494841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.138797</td>\n",
       "      <td>0.475728</td>\n",
       "      <td>0.119156</td>\n",
       "      <td>0.496151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lr_output_one  dt_output_one  rf_output_one  ab_output_one\n",
       "0       0.002691       0.049296       0.079142       0.487058\n",
       "1       0.173167       0.252351       0.128049       0.490910\n",
       "2       0.737308       0.903226       0.529802       0.500735\n",
       "3       0.436940       0.049296       0.125879       0.494841\n",
       "4       0.138797       0.475728       0.119156       0.496151"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#50640;&#51060;&#45796; &#48512;&#49828;&#53944; &#48516;&#47448;&#44592;&#50640; &#45824;&#54620; &#44208;&#44284;&#44050;&#51012;, &#48512;&#47448; 1&#51060; &#46112; &#54869;&#47456;&#47564; &#47532;&#53556;&#48155;&#49845;&#45768;&#45796;.\n",
    "ensemble['ab_output_one'] = ab_train_oof_pred\n",
    "ensemble.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensemble = pd.concat([ensemble, pd.DataFrame(y_train).reset_index(drop=True)], axis=1)"
   ]
  },
  {
   "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>lr_output_one</th>\n",
       "      <th>dt_output_one</th>\n",
       "      <th>rf_output_one</th>\n",
       "      <th>ab_output_one</th>\n",
       "      <th>Attrition_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002691</td>\n",
       "      <td>0.049296</td>\n",
       "      <td>0.079142</td>\n",
       "      <td>0.487058</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.173167</td>\n",
       "      <td>0.252351</td>\n",
       "      <td>0.128049</td>\n",
       "      <td>0.490910</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.737308</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.529802</td>\n",
       "      <td>0.500735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.436940</td>\n",
       "      <td>0.049296</td>\n",
       "      <td>0.125879</td>\n",
       "      <td>0.494841</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.138797</td>\n",
       "      <td>0.475728</td>\n",
       "      <td>0.119156</td>\n",
       "      <td>0.496151</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lr_output_one  dt_output_one  rf_output_one  ab_output_one  Attrition_idx\n",
       "0       0.002691       0.049296       0.079142       0.487058              0\n",
       "1       0.173167       0.252351       0.128049       0.490910              0\n",
       "2       0.737308       0.903226       0.529802       0.500735              1\n",
       "3       0.436940       0.049296       0.125879       0.494841              0\n",
       "4       0.138797       0.475728       0.119156       0.496151              0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. &#47700;&#53440; &#48516;&#47448;&#44592; &#54617;&#49845;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_classifier = LogisticRegression(fit_intercept=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=False,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_classifier.fit(\n",
    "    ensemble[['lr_output_one', 'dt_output_one', 'rf_output_one', 'ab_output_one']],\n",
    "    ensemble['Attrition_idx'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. &#53580;&#49828;&#53944; &#49483;&#50640; &#51201;&#50857; &#44032;&#45733;&#54616;&#46020;&#47197; &#53580;&#49828;&#53944; &#49483;&#50640; &#45824;&#54620; &#50696;&#52769;&#44050; &#44396;&#54616;&#44592;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensemble_test = pd.DataFrame()"
   ]
  },
  {
   "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></th>\n",
       "      <th>lr_output_one</th>\n",
       "      <th>dt_output_one</th>\n",
       "      <th>rf_output_one</th>\n",
       "      <th>ab_output_one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.162339</td>\n",
       "      <td>0.126429</td>\n",
       "      <td>0.226182</td>\n",
       "      <td>0.498060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.032265</td>\n",
       "      <td>0.113055</td>\n",
       "      <td>0.110086</td>\n",
       "      <td>0.493439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.457867</td>\n",
       "      <td>0.761797</td>\n",
       "      <td>0.484164</td>\n",
       "      <td>0.502463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.013984</td>\n",
       "      <td>0.057650</td>\n",
       "      <td>0.109196</td>\n",
       "      <td>0.495544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.125784</td>\n",
       "      <td>0.168591</td>\n",
       "      <td>0.132689</td>\n",
       "      <td>0.495352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lr_output_one  dt_output_one  rf_output_one  ab_output_one\n",
       "0       0.162339       0.126429       0.226182       0.498060\n",
       "1       0.032265       0.113055       0.110086       0.493439\n",
       "2       0.457867       0.761797       0.484164       0.502463\n",
       "3       0.013984       0.057650       0.109196       0.495544\n",
       "4       0.125784       0.168591       0.132689       0.495352"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# &#47196;&#51648;&#49828;&#54001;&#54924;&#44480; &#48516;&#47448;&#44592;&#50640; &#45824;&#54620; &#44208;&#44284;&#44050;&#51012;, &#48512;&#47448; 1&#51060; &#46112; &#54869;&#47456;&#47564; &#47532;&#53556;&#48155;&#49845;&#45768;&#45796;.\n",
    "ensemble_test['lr_output_one'] = lr_test_oof_pred['LogisticRegression_mean']\n",
    "ensemble_test['dt_output_one'] = dt_test_oof_pred['DecisionTreeClassifier_mean']\n",
    "ensemble_test['rf_output_one'] = rf_test_oof_pred['RandomForestClassifier_mean']\n",
    "ensemble_test['ab_output_one'] = ab_test_oof_pred['AdaBoostClassifier_mean']\n",
    "ensemble_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. &#47700;&#53440;&#48516;&#47448;&#44592; &#49457;&#45733; &#52769;&#51221;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# &#54617;&#49845; &#49483;&#50640; &#45824;&#54620; &#50696;&#52769;&#44050;\n",
    "train_pred = meta_classifier.predict(\n",
    "    ensemble[['lr_output_one', 'dt_output_one', 'rf_output_one', 'ab_output_one']],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# &#53580;&#49828;&#53944; &#49483;&#50640; &#45824;&#54620; &#50696;&#52769;&#44050;\n",
    "test_pred = meta_classifier.predict(\n",
    "    ensemble_test[['lr_output_one', 'dt_output_one', 'rf_output_one', 'ab_output_one']],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train Confusion Matrix :\n"
     ]
    },
    {
     "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>Predict</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>839</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>130</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0   1\n",
       "Actual          \n",
       "0        839  14\n",
       "1        130  46"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train accuracy : 0.8600583090379009\n",
      "\n",
      " Train Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.87      0.98      0.92       853\n",
      "          1       0.77      0.26      0.39       176\n",
      "\n",
      "avg / total       0.85      0.86      0.83      1029\n",
      "\n",
      "============================================================\n",
      "\n",
      " Test Confusion Matrix :\n"
     ]
    },
    {
     "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>Predict</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>376</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0   1\n",
       "Actual          \n",
       "0        376   4\n",
       "1         48  13"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy : 0.8820861678004536\n",
      "\n",
      " Test Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.99      0.94       380\n",
      "          1       0.76      0.21      0.33        61\n",
      "\n",
      "avg / total       0.87      0.88      0.85       441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "get_metric(y_train, train_pred, 'Train')\n",
    "print('=' * 60)\n",
    "get_metric(y_test, test_pred, 'Test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. &#44060;&#48324; &#48516;&#47448;&#44592;&#50640; &#45824;&#54620; &#54217;&#44032;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#44033;&#44033;&#51032; &#48516;&#47448;&#44592;&#50640; &#45824;&#54644;&#49436; &#54217;&#44032; : &#47196;&#51648;&#49828;&#54001; &#54924;&#44480; &#48516;&#47448;&#44592;&#47484; &#47700;&#53440; &#48516;&#47448;&#44592;&#47196; &#49324;&#50857;&#54664;&#51004;&#47732; &#44033; &#44228;&#49688;(coefficients)&#47484; &#49332;&#54196;&#48376;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.72324283,  0.07557159,  1.90903959, -5.85258971]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_classifier.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#50640;&#51060;&#45796;&#48512;&#49828;&#53944;&#51032; &#44221;&#50864; &#49457;&#45733;&#51012; &#45132;&#50612;&#45236;&#47532;&#44256; &#51080;&#45716;&#44163;&#52376;&#47100; &#48372;&#51064;&#45796;.\n",
    "\n",
    "&#46384;&#46972;&#49436;, &#50640;&#51060;&#45796; &#48512;&#49828;&#53944;&#51032; &#54028;&#46972;&#48120;&#53552;&#47484; &#51312;&#51208;&#54644;&#48372;&#44256; &#49457;&#45733;&#51012; &#49332;&#54196;&#48372;&#44144;&#45208;, &#50640;&#51060;&#45796; &#48512;&#49828;&#53944;&#47484; &#51228;&#44144;&#54616;&#44256; &#49457;&#45733;&#51012; &#49332;&#54196;&#48376;&#45796;."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## &#46041;&#51068; &#54805;&#49885; &#48516;&#47448;&#44592;&#47484; &#49324;&#50857;&#54620; &#48512;&#53944;&#49828;&#53944;&#47017; &#54364;&#48376;&#51012; &#53685;&#54620; &#50521;&#49345;&#48660;&#46308;&#51032; &#50521;&#49345;&#48660;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- &#54984;&#47144; &#45936;&#51060;&#53552;&#50640;&#49436; &#48512;&#53944;&#49828;&#53944;&#47017; &#54364;&#48376;&#51012; &#52628;&#52636;&#54620;&#45796;.\n",
    "- &#44033; &#54364;&#48376;&#50640; &#44288;&#54644; &#47588;&#48264; &#50640;&#51060;&#45796; &#48512;&#49828;&#53944; &#47784;&#45944;&#51012; &#54617;&#49845;&#54620;&#45796;.\n",
    "- &#44060;&#48324; &#50640;&#51060;&#45796; &#48512;&#49828;&#53944; &#47784;&#45944;&#51032; &#44208;&#44284;&#45716; bagging &#48516;&#47448;&#44592;&#47484; &#53685;&#54644; &#54633;&#52432;&#51652;&#45796;.\n",
    "\n",
    "&#48516;&#49328;&#51032; &#52629;&#49548;&#44032; &#50668;&#51204;&#55176; &#49457;&#45733;&#51012; &#54693;&#49345;&#49884;&#53412;&#45716; &#47588;&#50864; &#50976;&#50672;&#54620; &#47784;&#45944;&#50640; &#44288;&#54644; &#51201;&#51208;&#54616;&#45796;(&#51032;&#49324; &#44208;&#51221; &#53944;&#47532;, &#47004;&#45924; &#54252;&#47112;&#49828;&#53944; &#46321;)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. &#44592;&#48376; &#48516;&#47448;&#44592; &#48143; &#48512;&#53944;&#49828;&#53944;&#47017; &#49368;&#54540;&#51012; &#54617;&#49845;&#49884;&#53420; &#47784;&#45944; &#49373;&#49457;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# &#50640;&#51060;&#45796; &#48512;&#49828;&#53944; &#47784;&#45944;&#50640; &#49324;&#50857;&#54624; &#44592;&#48376; &#48516;&#47448;&#44592;(&#51032;&#49324;&#44208;&#51221; &#44536;&#47336;&#53552;&#44592;)\n",
    "base_learner = DecisionTreeClassifier(max_depth=1, class_weight=c_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# &#48512;&#53944;&#49828;&#53944;&#47017; &#49368;&#54540;&#51012; &#54617;&#49845;&#49884;&#53420; &#50640;&#51060;&#45796; &#48512;&#49828;&#53944; &#47784;&#45944;\n",
    "ab_classifier = AdaBoostClassifier(base_estimator=base_learner,\n",
    "                                   n_estimators=500,\n",
    "                                   learning_rate=0.05,\n",
    "                                   random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R',\n",
       "          base_estimator=DecisionTreeClassifier(class_weight={0: 0.3, 1: 0.7}, criterion='gini',\n",
       "            max_depth=1, max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best'),\n",
       "          learning_rate=0.05, n_estimators=500, random_state=42)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&#50640;&#51060;&#45796; &#48512;&#49828;&#53944;&#51032; &#44592;&#48376;&#49457;&#45733;&#51012; &#49332;&#54196;&#48372;&#51088;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred = ab_classifier.predict(x_train)\n",
    "test_pred = ab_classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train Confusion Matrix :\n"
     ]
    },
    {
     "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>Predict</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>806</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0    1\n",
       "Actual           \n",
       "0        806   47\n",
       "1         63  113"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train accuracy : 0.8931000971817298\n",
      "\n",
      " Train Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.94      0.94       853\n",
      "          1       0.71      0.64      0.67       176\n",
      "\n",
      "avg / total       0.89      0.89      0.89      1029\n",
      "\n",
      "============================================================\n",
      "\n",
      " Test Confusion Matrix :\n"
     ]
    },
    {
     "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>Predict</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>349</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0   1\n",
       "Actual          \n",
       "0        349  31\n",
       "1         34  27"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy : 0.8526077097505669\n",
      "\n",
      " Test Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.92      0.91       380\n",
      "          1       0.47      0.44      0.45        61\n",
      "\n",
      "avg / total       0.85      0.85      0.85       441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "get_metric(y_train, train_pred, 'Train')\n",
    "print('=' * 60)\n",
    "get_metric(y_test, test_pred, 'Test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Bootstrapping samples&#47484; &#49373;&#49457;&#54616;&#44256; &#44033; &#49368;&#54540;&#50640; &#48516;&#47448;&#44592;&#47484; &#54617;&#49845;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`BaggingClassifier`&#45716; &#45236;&#48512;&#50640; bootstrap &#44592;&#45733;&#51060; &#51080;&#44256;, &#54617;&#49845;&#49884;&#53420; &#48516;&#47448;&#44592;&#47484; 1&#44060; &#51648;&#51221;&#54624; &#49688; &#51080;&#45796;. &#46384;&#46972;&#49436; &#51060;&#44163;&#51012; &#49324;&#50857;&#54616;&#47732; &#46108;&#45796;."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "bag_classifier = BaggingClassifier(\n",
    "    base_estimator=ab_classifier,\n",
    "    n_estimators = 50,\n",
    "    max_samples=1.0,\n",
    "    max_features=1.0,\n",
    "    bootstrap=True,\n",
    "    bootstrap_features=False,\n",
    "    n_jobs=-1,\n",
    "    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=AdaBoostClassifier(algorithm='SAMME.R',\n",
       "          base_estimator=DecisionTreeClassifier(class_weight={0: 0.3, 1: 0.7}, criterion='gini',\n",
       "            max_depth=1, max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_sam...None,\n",
       "            splitter='best'),\n",
       "          learning_rate=0.05, n_estimators=500, random_state=42),\n",
       "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=1.0, n_estimators=50, n_jobs=-1, oob_score=False,\n",
       "         random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. &#49457;&#45733; &#52769;&#51221;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred = bag_classifier.predict(x_train)\n",
    "test_pred = bag_classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train Confusion Matrix :\n"
     ]
    },
    {
     "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>Predict</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>824</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0    1\n",
       "Actual           \n",
       "0        824   29\n",
       "1         69  107"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Train accuracy : 0.9047619047619048\n",
      "\n",
      " Train Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.97      0.94       853\n",
      "          1       0.79      0.61      0.69       176\n",
      "\n",
      "avg / total       0.90      0.90      0.90      1029\n",
      "\n",
      "============================================================\n",
      "\n",
      " Test Confusion Matrix :\n"
     ]
    },
    {
     "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Predict</th>\n",
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       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>359</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>25</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predict    0   1\n",
       "Actual          \n",
       "0        359  21\n",
       "1         36  25"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy : 0.8707482993197279\n",
      "\n",
      " Test Classification Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.94      0.93       380\n",
      "          1       0.54      0.41      0.47        61\n",
      "\n",
      "avg / total       0.86      0.87      0.86       441\n",
      "\n"
     ]
    }
   ],
   "source": [
    "get_metric(y_train, train_pred, 'Train')\n",
    "print('=' * 60)\n",
    "get_metric(y_test, test_pred, 'Test')"
   ]
  }
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