11. K- Fold Cross Validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## It helps us to determine which Model should we use for a data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-fold cross validation guarantees that the score of our model does not depend on the way we picked the train and test set. The data set is divided into k number of subsets and the holdout method is repeated k number of times."
   ]
  },
  {
   "attachments": {
    "K_FOLD.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![K_FOLD.png](attachment:K_FOLD.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let us go through this in steps:\n",
    "\n",
    "->**Randomly** split your entire dataset into k number of folds (subsets)\n",
    "\n",
    "->For each fold in your dataset, build your model on k – 1 folds of the dataset. Then, test the model to check the effectiveness for kth fold\n",
    "\n",
    "->Repeat this until each of the k-folds has served as the test set\n",
    "\n",
    "->The average of your k recorded accuracy is called the cross-validation accuracy and will serve as your performance metric for the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=load_digits()\n",
    "dir(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## importing models\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.925     , 0.875     , 0.93871866, 0.93314763, 0.89693593])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(LogisticRegression(max_iter=10000),data.data,data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.79555556, 0.89333333, 0.67555556, 0.77777778, 0.83555556,\n",
       "       0.88839286, 0.87053571, 0.75      ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cv determines number of folds # 8 values are there in array \n",
    "cross_val_score(DecisionTreeClassifier(),data.data,data.target,cv=8)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.90833333, 0.90277778, 0.94986072, 0.95264624, 0.89972145])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(RandomForestClassifier(n_estimators=30),data.data,data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.96111111, 0.94444444, 0.98328691, 0.98885794, 0.93871866])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(SVC(),data.data,data.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Regressor for Trees**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.79651926, 0.81644182, 0.83170095, 0.85034193, 0.77035186])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(RandomForestRegressor(),data.data,data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.48786052, 0.55046763, 0.61970483, 0.48545446, 0.58847679])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(DecisionTreeRegressor(),data.data,data.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using diabetes data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"/Users/sudhanshukumar/Documents/Machine Learning/0 csv files/diabetes.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(\"Outcome\",axis=1)\n",
    "y=df.Outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77272727, 0.74675325, 0.75324675, 0.81045752, 0.77777778])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(LogisticRegression(max_iter=1000),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.68181818, 0.68181818, 0.67532468, 0.77777778, 0.7124183 ])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(DecisionTreeClassifier(),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.76623377, 0.72077922, 0.74675325, 0.82352941, 0.75816993])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(RandomForestClassifier(n_estimators=100),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.74675325, 0.73376623, 0.77272727, 0.79084967, 0.75163399])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(SVC(),X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Regressor for Trees**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.31185185, -0.52395833, -0.33695062, -0.17004049, -0.17340067])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(DecisionTreeRegressor(),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.23305719, 0.18296583, 0.35964243, 0.32662794, 0.25229478])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(RandomForestRegressor(),X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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