22-MODEL EVALUATION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation : Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to talk about model evaluation.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=iris.data,iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(112, 4)\n",
      "(38, 4)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg=LogisticRegression(solver='lbfgs',multi_class='auto',max_iter=1000)\n",
    "logreg.fit(X_train,y_train)\n",
    "logreg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.96666667, 1.        , 0.93333333, 0.96666667, 1.        ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores=cross_val_score(logreg, X,y,cv=5)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9733333333333334"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mglearn\n",
    "mglearn.plots.plot_stratified_cross_validation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold=KFold(n_splits=3,shuffle=True,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98, 0.96, 0.96])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(logreg,iris.data,iris.target,cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "loo=LeaveOneOut()\n",
    "scores=cross_val_score(logreg,iris.data,iris.target,cv=loo)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GroupKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=make_blobs(n_samples=12, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "groups=[0,0,0,1,1,1,1,2,2,3,3,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75      , 0.6       , 0.66666667])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores=cross_val_score(logreg,X,y,groups,cv=GroupKFold(n_splits=3))\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
 ],
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