24-Evaluation Metrics .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation Metrics "
   ]
  },
  {
   "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 evaluation metrics.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummy Predictions"
   ]
  },
  {
   "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": [
    "y[y!=1]=-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.631578947368421"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc=SVC(kernel='linear',C=1).fit(X_train,y_train)\n",
    "svc.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5789473684210527"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=DummyClassifier(strategy='most_frequent',random_state=0).fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9473684210526315"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc=SVC(kernel='rbf',gamma='scale',C=1).fit(X_train,y_train)\n",
    "svc.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits=load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=digits.target==9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(digits.data,y,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg=LogisticRegression(solver='liblinear',C=0.1).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_logreg=logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[401,   2],\n",
       "       [  8,  39]], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,pred_logreg)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mglearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_confusion_matrix_illustration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "mglearn.plots.plot_binary_confusion_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision, recall, and f-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    not nine       0.98      1.00      0.99       403\n",
      "        nine       0.95      0.83      0.89        47\n",
      "\n",
      "    accuracy                           0.98       450\n",
      "   macro avg       0.97      0.91      0.94       450\n",
      "weighted avg       0.98      0.98      0.98       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_logreg, target_names=['not nine','nine']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Receiver operating characteristic (ROC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds=roc_curve(y_test, logreg.decision_function(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'TPR(recall)')"
      ]
     },
     "execution_count": 30,
     "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": [
    "plt.plot(fpr,tpr,label='ROC Curve')\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR(recall)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiclass classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(digits.data,digits.target,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LogisticRegression(solver='liblinear',multi_class='auto').fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9533333333333334\n",
      "[[37  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 39  0  0  0  0  2  0  2  0]\n",
      " [ 0  0 41  3  0  0  0  0  0  0]\n",
      " [ 0  0  1 43  0  0  0  0  0  1]\n",
      " [ 0  0  0  0 38  0  0  0  0  0]\n",
      " [ 0  1  0  0  0 47  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 52  0  0  0]\n",
      " [ 0  1  0  1  1  0  0 45  0  0]\n",
      " [ 0  3  1  0  0  0  0  0 43  1]\n",
      " [ 0  0  0  1  0  1  0  0  1 44]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,pred))\n",
    "cm=confusion_matrix(y_test,pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(cm,index=digits.target_names,columns=digits.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "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.heatmap(df,annot=True,cbar=None,cmap='Blues')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('True Class')\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.show()"
   ]
  },
  {
   "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) 😎"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}