Imbalance and Cross-Validation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "030dccf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Machine-learning\\\\Assignments'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2678da05",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"F:\\\\Machine-learning\\\\Assignments\\\\data_set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a460cdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21b790e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Customer-Churn-Prediction.csv\")\n",
    "df1 = df.copy()\n",
    "df2 = df.copy()\n",
    "df3 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23334bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5330b6ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d6cd84db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          0\n",
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a581724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     5174\n",
       "Yes    1869\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3adeffe3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Churn', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Churn'], color=\"#2B00FF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80520030",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58d73ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "202f34ee",
   "metadata": {},
   "source": [
    "# Basic Visualization using Seaborn Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e038afe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='gender'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='gender', data=df, hue='Churn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b927adbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'gender', hue='Churn', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d0a547c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='OnlineSecurity', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'OnlineSecurity',hue='Churn', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0bd24414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', 'No internet service'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OnlineSecurity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cfadfd02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No                     3498\n",
       "Yes                    2019\n",
       "No internet service    1526\n",
       "Name: OnlineSecurity, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OnlineSecurity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b9092682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PaymentMethod', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10,6)) \n",
    "sns.countplot(x = 'PaymentMethod',hue='Churn', data=df) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e788bd1",
   "metadata": {},
   "source": [
    "# Preprocess the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f688baad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop Customer id\n",
    "df = df.drop('customerID', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c3d65478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 20)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3a0d8ff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "10ee5e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0c042fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.core.dtypes.common import is_numeric_dtype\n",
    "\n",
    "for column in df.columns:\n",
    "    if is_numeric_dtype(df[column]):\n",
    "        continue\n",
    "    else:\n",
    "        df[column] = label.fit_transform(df[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "42125bb6",
   "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>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>2</td>\n",
       "      <td>29.85</td>\n",
       "      <td>2505</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1466</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>3</td>\n",
       "      <td>53.85</td>\n",
       "      <td>157</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>925</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0              1                0               0             2   \n",
       "1              0                0               2             0   \n",
       "2              0                0               2             2   \n",
       "3              1                0               2             0   \n",
       "4              0                1               0             0   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 0            0            0                0         0   \n",
       "1                 2            0            0                0         1   \n",
       "2                 0            0            0                0         0   \n",
       "3                 2            2            0                0         1   \n",
       "4                 0            0            0                0         0   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
       "0                 1              2           29.85          2505      0  \n",
       "1                 0              3           56.95          1466      0  \n",
       "2                 1              3           53.85           157      1  \n",
       "3                 0              0           42.30          1400      0  \n",
       "4                 1              2           70.70           925      1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c6062e",
   "metadata": {},
   "source": [
    "# Split dataset into x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7befa907",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop('Churn',axis=1)\n",
    "y = df['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4d18dcaf",
   "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>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>29.85</td>\n",
       "      <td>2505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>53.85</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0              1                0               0             2   \n",
       "1              0                0               2             0   \n",
       "2              0                0               2             2   \n",
       "3              1                0               2             0   \n",
       "4              0                1               0             0   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 0            0            0                0         0   \n",
       "1                 2            0            0                0         1   \n",
       "2                 0            0            0                0         0   \n",
       "3                 2            2            0                0         1   \n",
       "4                 0            0            0                0         0   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  \n",
       "0                 1              2           29.85          2505  \n",
       "1                 0              3           56.95          1466  \n",
       "2                 1              3           53.85           157  \n",
       "3                 0              0           42.30          1400  \n",
       "4                 1              2           70.70           925  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "92ac3eaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 19)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1e4f008d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "689f1721",
   "metadata": {},
   "source": [
    "# Imbalanced Data Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "58e00568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5174\n",
       "1    1869\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Churn.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "35e300c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "churned = (5174/(5174+1869)) * 100\n",
    "not_churned = (1869/(5174+1869)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b0a31e80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not churned 26.536987079369588 percent of total people\n",
      "Churned 73.4630129206304 percent of total people\n"
     ]
    }
   ],
   "source": [
    "print('Not churned {} percent of total people'.format(not_churned))\n",
    "print('Churned {} percent of total people'.format(churned))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "87a367cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_no = df[df['Churn']==0]\n",
    "churn_yes = df[df['Churn']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "598d6c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5174, 20)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_no.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cf45dd4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1869, 20)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_yes.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a498905d",
   "metadata": {},
   "source": [
    "# Synthetic Minority Oversampling Technique (SMOTETomek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fe7584a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.combine import SMOTETomek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "97e03ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "smo = SMOTETomek(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4baea0bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_smo,Y_smo = smo.fit_resample(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7fe9ed57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9612, 19)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_smo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a24fcfb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9612,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_smo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "802da9eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4806\n",
       "1    4806\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_smo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2ab6b540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
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       "      <th>OnlineBackup</th>\n",
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       "      <th>TotalCharges</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>3</td>\n",
       "      <td>53.85</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>70.70</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       0              0        1           0       1             0   \n",
       "1       1              0        0           0      34             1   \n",
       "2       1              0        0           0       2             1   \n",
       "3       1              0        0           0      45             0   \n",
       "4       0              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0              1                0               0             2   \n",
       "1              0                0               2             0   \n",
       "2              0                0               2             2   \n",
       "3              1                0               2             0   \n",
       "4              0                1               0             0   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 0            0            0                0         0   \n",
       "1                 2            0            0                0         1   \n",
       "2                 0            0            0                0         0   \n",
       "3                 2            2            0                0         1   \n",
       "4                 0            0            0                0         0   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  \n",
       "0                 1              2           29.85          2505  \n",
       "1                 0              3           56.95          1466  \n",
       "2                 1              3           53.85           157  \n",
       "3                 0              0           42.30          1400  \n",
       "4                 1              2           70.70           925  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_smo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7678cab0",
   "metadata": {},
   "source": [
    "# Under_Sampling by Near Miss "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8f5e9114",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import NearMiss\n",
    "nm = NearMiss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5e61c6f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_nm , y_nm = nm.fit_resample(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "77cd4937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3738, 19)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_nm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b13a9db5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3738,)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_nm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "47bdaa0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1869\n",
       "1    1869\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_nm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "27b5c5c8",
   "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>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>20.9</td>\n",
       "      <td>1624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>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>2</td>\n",
       "      <td>69.9</td>\n",
       "      <td>5454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>20.2</td>\n",
       "      <td>1610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>20.2</td>\n",
       "      <td>1610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>20.2</td>\n",
       "      <td>1610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       0              0        0           0       1             1   \n",
       "1       1              0        0           0       1             1   \n",
       "2       1              0        0           0       1             1   \n",
       "3       1              0        0           0       1             1   \n",
       "4       1              0        0           0       1             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0              0                2               1             1   \n",
       "1              0                1               0             0   \n",
       "2              0                2               1             1   \n",
       "3              0                2               1             1   \n",
       "4              0                2               1             1   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 1            1            1                1         0   \n",
       "1                 0            0            0                0         0   \n",
       "2                 1            1            1                1         0   \n",
       "3                 1            1            1                1         0   \n",
       "4                 1            1            1                1         0   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  \n",
       "0                 0              3            20.9          1624  \n",
       "1                 1              2            69.9          5454  \n",
       "2                 0              3            20.2          1610  \n",
       "3                 0              3            20.2          1610  \n",
       "4                 0              3            20.2          1610  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_nm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3939ad66",
   "metadata": {},
   "source": [
    "# Over Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3cb1dc9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "over_sample = RandomOverSampler(random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "be2d8479",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_new, y_new = over_sample.fit_resample(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e048b24b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10348, 19)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "415505b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10348,)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "286451d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5174\n",
       "1    5174\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_new.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "696ff683",
   "metadata": {},
   "source": [
    "# 1. Cross Validation and accuracy using SMOTETomek"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3b36d76",
   "metadata": {},
   "source": [
    "# Hold Out Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e54343fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4b6cee0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest, ytrain,ytest = train_test_split(X_smo, Y_smo, train_size=.70,random_state=42) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "08dc63ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain: (6728, 19), xtest: (2884, 19) \n",
      "ytrain: (6728,),  ytest: (2884,)\n"
     ]
    }
   ],
   "source": [
    "print(f\"xtrain: {xtrain.shape}, xtest: {xtest.shape} \\nytrain: {ytrain.shape},  ytest: {ytest.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2caa21e",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "92c6df38",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "import xgboost \n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "05d85e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f87ec198",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:52:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "212be7e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 1, 1, 0])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_pred = xgb.predict(xtest)\n",
    "xgb_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9031f218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8647711511789181"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.score(xtest,ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89c10595",
   "metadata": {},
   "source": [
    "# K Fold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9420ddff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3480b02b",
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6b110b65",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:55:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:55:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:55:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:55:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:55:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "result = cross_val_score(xgb, X_smo, Y_smo, cv=kfold) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7c8a1cbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81175247, 0.79771191, 0.80072841, 0.9157128 , 0.96149844])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7f7e6f9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9614984391259105"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "890d8874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7977119084763391"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e95f1ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8574808049554032"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b72711d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d16cac2a",
   "metadata": {},
   "source": [
    "# Stratified k fold cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c9b54a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "336d9b1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "st = StratifiedKFold(n_splits=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "51a8f972",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:02:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:02:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:02:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:02:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "result2 = cross_val_score(xgb, X_smo, Y_smo, cv=st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "88e7e4aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.71535581, 0.85018727, 0.8934665 , 0.92051602])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "36f5033b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9205160216396171"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1a67441a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7153558052434457"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c1e11b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8448813982521848"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f73ab3eb",
   "metadata": {},
   "source": [
    "# Leave One Out Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3dcf9256",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import LeaveOneOut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6b3cbaf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LeaveOneOut()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e007d97e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:27:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:27:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:27:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:27:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[1;32mIn [80]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m result3 \u001b[38;5;241m=\u001b[39m cross_val_score(xgb, X_smo, Y_smo, cv\u001b[38;5;241m=\u001b[39mle)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:509\u001b[0m, in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m    506\u001b[0m \u001b[38;5;66;03m# To ensure multimetric format is not supported\u001b[39;00m\n\u001b[0;32m    507\u001b[0m scorer \u001b[38;5;241m=\u001b[39m check_scoring(estimator, scoring\u001b[38;5;241m=\u001b[39mscoring)\n\u001b[1;32m--> 509\u001b[0m cv_results \u001b[38;5;241m=\u001b[39m \u001b[43mcross_validate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    510\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    511\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    512\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    513\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    514\u001b[0m \u001b[43m    \u001b[49m\u001b[43mscoring\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mscore\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mscorer\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    515\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    516\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    517\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    518\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfit_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    519\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpre_dispatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpre_dispatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    520\u001b[0m \u001b[43m    \u001b[49m\u001b[43merror_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merror_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    521\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    522\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cv_results[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_score\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:267\u001b[0m, in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    264\u001b[0m \u001b[38;5;66;03m# We clone the estimator to make sure that all the folds are\u001b[39;00m\n\u001b[0;32m    265\u001b[0m \u001b[38;5;66;03m# independent, and that it is pickle-able.\u001b[39;00m\n\u001b[0;32m    266\u001b[0m parallel \u001b[38;5;241m=\u001b[39m Parallel(n_jobs\u001b[38;5;241m=\u001b[39mn_jobs, verbose\u001b[38;5;241m=\u001b[39mverbose, pre_dispatch\u001b[38;5;241m=\u001b[39mpre_dispatch)\n\u001b[1;32m--> 267\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    268\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_and_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    269\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    270\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    271\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    272\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscorers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    273\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    274\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    275\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    276\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    277\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfit_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    278\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_train_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_train_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    279\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    280\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_estimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_estimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    281\u001b[0m \u001b[43m        \u001b[49m\u001b[43merror_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merror_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    282\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    283\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    284\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    286\u001b[0m _warn_about_fit_failures(results, error_score)\n\u001b[0;32m    288\u001b[0m \u001b[38;5;66;03m# For callabe scoring, the return type is only know after calling. If the\u001b[39;00m\n\u001b[0;32m    289\u001b[0m \u001b[38;5;66;03m# return type is a dictionary, the error scores can now be inserted with\u001b[39;00m\n\u001b[0;32m    290\u001b[0m \u001b[38;5;66;03m# the correct key.\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\parallel.py:1046\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdispatch_one_batch(iterator):\n\u001b[0;32m   1044\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterating \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_original_iterator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1046\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdispatch_one_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m   1047\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m   1049\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pre_dispatch \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m n_jobs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   1050\u001b[0m     \u001b[38;5;66;03m# The iterable was consumed all at once by the above for loop.\u001b[39;00m\n\u001b[0;32m   1051\u001b[0m     \u001b[38;5;66;03m# No need to wait for async callbacks to trigger to\u001b[39;00m\n\u001b[0;32m   1052\u001b[0m     \u001b[38;5;66;03m# consumption.\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\parallel.py:861\u001b[0m, in \u001b[0;36mParallel.dispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    859\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m    860\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 861\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    862\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\parallel.py:779\u001b[0m, in \u001b[0;36mParallel._dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    777\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m    778\u001b[0m     job_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs)\n\u001b[1;32m--> 779\u001b[0m     job \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_async\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    780\u001b[0m     \u001b[38;5;66;03m# A job can complete so quickly than its callback is\u001b[39;00m\n\u001b[0;32m    781\u001b[0m     \u001b[38;5;66;03m# called before we get here, causing self._jobs to\u001b[39;00m\n\u001b[0;32m    782\u001b[0m     \u001b[38;5;66;03m# grow. To ensure correct results ordering, .insert is\u001b[39;00m\n\u001b[0;32m    783\u001b[0m     \u001b[38;5;66;03m# used (rather than .append) in the following line\u001b[39;00m\n\u001b[0;32m    784\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs\u001b[38;5;241m.\u001b[39minsert(job_idx, job)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\_parallel_backends.py:208\u001b[0m, in \u001b[0;36mSequentialBackend.apply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_async\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, callback\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    207\u001b[0m     \u001b[38;5;124;03m\"\"\"Schedule a func to be run\"\"\"\u001b[39;00m\n\u001b[1;32m--> 208\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mImmediateResult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m callback:\n\u001b[0;32m    210\u001b[0m         callback(result)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\_parallel_backends.py:572\u001b[0m, in \u001b[0;36mImmediateResult.__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    569\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, batch):\n\u001b[0;32m    570\u001b[0m     \u001b[38;5;66;03m# Don't delay the application, to avoid keeping the input\u001b[39;00m\n\u001b[0;32m    571\u001b[0m     \u001b[38;5;66;03m# arguments in memory\u001b[39;00m\n\u001b[1;32m--> 572\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresults \u001b[38;5;241m=\u001b[39m \u001b[43mbatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\parallel.py:262\u001b[0m, in \u001b[0;36mBatchedCalls.__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    258\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    259\u001b[0m     \u001b[38;5;66;03m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[0;32m    260\u001b[0m     \u001b[38;5;66;03m# change the default number of processes to -1\u001b[39;00m\n\u001b[0;32m    261\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m parallel_backend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_jobs):\n\u001b[1;32m--> 262\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m [func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    263\u001b[0m                 \u001b[38;5;28;01mfor\u001b[39;00m func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems]\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\joblib\\parallel.py:262\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    258\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    259\u001b[0m     \u001b[38;5;66;03m# Set the default nested backend to self._backend but do not set the\u001b[39;00m\n\u001b[0;32m    260\u001b[0m     \u001b[38;5;66;03m# change the default number of processes to -1\u001b[39;00m\n\u001b[0;32m    261\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m parallel_backend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_jobs):\n\u001b[1;32m--> 262\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m [func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    263\u001b[0m                 \u001b[38;5;28;01mfor\u001b[39;00m func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems]\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\utils\\fixes.py:216\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig):\n\u001b[1;32m--> 216\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:680\u001b[0m, in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[0;32m    678\u001b[0m         estimator\u001b[38;5;241m.\u001b[39mfit(X_train, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[0;32m    679\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 680\u001b[0m         estimator\u001b[38;5;241m.\u001b[39mfit(X_train, y_train, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[0;32m    682\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m    683\u001b[0m     \u001b[38;5;66;03m# Note fit time as time until error\u001b[39;00m\n\u001b[0;32m    684\u001b[0m     fit_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m start_time\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\core.py:506\u001b[0m, in \u001b[0;36m_deprecate_positional_args.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    504\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args):\n\u001b[0;32m    505\u001b[0m     kwargs[k] \u001b[38;5;241m=\u001b[39m arg\n\u001b[1;32m--> 506\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m f(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1250\u001b[0m, in \u001b[0;36mXGBClassifier.fit\u001b[1;34m(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)\u001b[0m\n\u001b[0;32m   1230\u001b[0m model, feval, params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_configure_fit(xgb_model, eval_metric, params)\n\u001b[0;32m   1231\u001b[0m train_dmatrix, evals \u001b[38;5;241m=\u001b[39m _wrap_evaluation_matrices(\n\u001b[0;32m   1232\u001b[0m     missing\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmissing,\n\u001b[0;32m   1233\u001b[0m     X\u001b[38;5;241m=\u001b[39mX,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1247\u001b[0m     label_transform\u001b[38;5;241m=\u001b[39mlabel_transform,\n\u001b[0;32m   1248\u001b[0m )\n\u001b[1;32m-> 1250\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_Booster \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1251\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1252\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_dmatrix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1253\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_num_boosting_rounds\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1254\u001b[0m \u001b[43m    \u001b[49m\u001b[43mevals\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1255\u001b[0m \u001b[43m    \u001b[49m\u001b[43mearly_stopping_rounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mearly_stopping_rounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1256\u001b[0m \u001b[43m    \u001b[49m\u001b[43mevals_result\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevals_result\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1257\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1258\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1259\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1260\u001b[0m \u001b[43m    \u001b[49m\u001b[43mxgb_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1261\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1262\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1264\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m callable(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobjective):\n\u001b[0;32m   1265\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobjective \u001b[38;5;241m=\u001b[39m params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobjective\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\training.py:188\u001b[0m, in \u001b[0;36mtrain\u001b[1;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks)\u001b[0m\n\u001b[0;32m    115\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrain\u001b[39m(params, dtrain, num_boost_round\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m, evals\u001b[38;5;241m=\u001b[39m(), obj\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, feval\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    116\u001b[0m           maximize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, early_stopping_rounds\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, evals_result\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    117\u001b[0m           verbose_eval\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, xgb_model\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, callbacks\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    118\u001b[0m     \u001b[38;5;66;03m# pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init\u001b[39;00m\n\u001b[0;32m    119\u001b[0m     \u001b[38;5;124;03m\"\"\"Train a booster with given parameters.\u001b[39;00m\n\u001b[0;32m    120\u001b[0m \n\u001b[0;32m    121\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    186\u001b[0m \u001b[38;5;124;03m    Booster : a trained booster model\u001b[39;00m\n\u001b[0;32m    187\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 188\u001b[0m     bst \u001b[38;5;241m=\u001b[39m \u001b[43m_train_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    189\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mnum_boost_round\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_boost_round\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    190\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mevals\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevals\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    191\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    192\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mxgb_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxgb_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    193\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mverbose_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    194\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mevals_result\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevals_result\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    195\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mmaximize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaximize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    196\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mearly_stopping_rounds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mearly_stopping_rounds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    197\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m bst\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\training.py:81\u001b[0m, in \u001b[0;36m_train_internal\u001b[1;34m(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks, evals_result, maximize, verbose_eval, early_stopping_rounds)\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m callbacks\u001b[38;5;241m.\u001b[39mbefore_iteration(bst, i, dtrain, evals):\n\u001b[0;32m     80\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m---> 81\u001b[0m \u001b[43mbst\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m callbacks\u001b[38;5;241m.\u001b[39mafter_iteration(bst, i, dtrain, evals):\n\u001b[0;32m     83\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\core.py:1680\u001b[0m, in \u001b[0;36mBooster.update\u001b[1;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[0;32m   1677\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_features(dtrain)\n\u001b[0;32m   1679\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fobj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1680\u001b[0m     _check_call(\u001b[43m_LIB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mXGBoosterUpdateOneIter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1681\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mctypes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mc_int\u001b[49m\u001b[43m(\u001b[49m\u001b[43miteration\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1682\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mdtrain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m   1683\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1684\u001b[0m     pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict(dtrain, output_margin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "result3 = cross_val_score(xgb, X_smo, Y_smo, cv=le)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "583e3ce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2093c2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96210ce2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dc0067f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9b30c7bd",
   "metadata": {},
   "source": [
    "# 2. Cross Validation and accuracy using Under_Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "711d5ce0",
   "metadata": {},
   "source": [
    "# Hold Out Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d55c2ccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(x_nm , y_nm, train_size=.70,random_state=42)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a3d685d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain: (2616, 19), xtest: (1122, 19) \n",
      "ytrain: (2616,),  ytest: (1122,)\n"
     ]
    }
   ],
   "source": [
    "print(f\"xtrain: {xtrain.shape}, xtest: {xtest.shape} \\nytrain: {ytrain.shape},  ytest: {ytest.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8ee57008",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost \n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d7d4b36f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb2 = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "19ee2485",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:27:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb2.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "28567c11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb2_pred = xgb2.predict(xtest)\n",
    "xgb2_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4e8279a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6586452762923352"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb2.score(xtest, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e4d3c98",
   "metadata": {},
   "source": [
    "# K Fold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "007e60a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "91842acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold1 = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "41713557",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "result4 = cross_val_score(xgb2, x_nm, y_nm, cv=kfold1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "500a79f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.31417112, 0.50534759, 0.61229947, 0.50736278, 0.53547523])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0a9f22a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3141711229946524"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result4.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "dde6ef57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6122994652406417"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result4.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "408c9f61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.494931240111963"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result4.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5061f1e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d599b0dd",
   "metadata": {},
   "source": [
    "# Stratified k fold cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "9f35e875",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "aa514c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "st2 = StratifiedKFold(n_splits=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "544984d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "result6 = cross_val_score(xgb2, x_nm, y_nm, cv=st2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "98e8b4c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.50160428, 0.62566845, 0.6627409 , 0.61777302])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "677b661f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5016042780748663"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result6.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "847fef3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6627408993576017"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result6.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d76187dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6019466614755694"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result6.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ad609b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a52ee9f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "103b2367",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fadbec8a",
   "metadata": {},
   "source": [
    "# 3. Cross Validation and accuracy using Over_Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7af794a",
   "metadata": {},
   "source": [
    "# Hold Out Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3e2edc45",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest, ytrain,ytest = train_test_split(x_new, y_new, train_size=.70,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "fc61133e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain: (7243, 19), xtest: (3105, 19) \n",
      "ytrain: (7243,),  ytest: (3105,)\n"
     ]
    }
   ],
   "source": [
    "print(f\"xtrain: {xtrain.shape}, xtest: {xtest.shape} \\nytrain: {ytrain.shape},  ytest: {ytest.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "44eb4eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost \n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "90f82e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb3 = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a4e52b64",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb3.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "256d9574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 1, 1, 0])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb3_pred = xgb3.predict(xtest)\n",
    "xgb3_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "773b4b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8428341384863124"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb3.score(xtest, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae25ba5c",
   "metadata": {},
   "source": [
    "# K Fold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "8e8a15c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d7ba1fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold2 = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "55806114",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:28:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "result5 = cross_val_score(xgb3, x_new, y_new, cv=kfold2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "06af182d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81256039, 0.8       , 0.79565217, 0.89463509, 0.88980184])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "83a84b6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7956521739130434"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result5.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "18934fe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8946350894151764"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result5.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "aecf606e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8385298972875411"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result5.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa279855",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7fd6410f",
   "metadata": {},
   "source": [
    "# Stratified k fold cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "93df1b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "587237d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "st3 = StratifiedKFold(n_splits=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a3c9b286",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:29:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:29:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:29:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:29:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "result7 = cross_val_score(xgb3, x_new, y_new, cv=st3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "62d7a4c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84808659, 0.8562041 , 0.85890993, 0.8867414 ])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "f4f03a31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8480865867800541"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result7.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "99ee24fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8867413993042134"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result7.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "da132028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8624855044453035"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result7.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f07a29f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7472476",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "730ce576",
   "metadata": {},
   "source": [
    "#### Leave One Out Cross Validation is so time-consuming algorithm, that's why I omit it for over and under-sampling.\n",
    "#### But I've done it for SMOTETomek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1fdabdc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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