notebooks/20_plot_confusion_matrix.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open in Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/justmarkham/scikit-learn-tips/master?filepath=notebooks%2F20_plot_confusion_matrix.ipynb)\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/justmarkham/scikit-learn-tips/blob/master/notebooks/20_plot_confusion_matrix.ipynb)\n",
    "\n",
    "# 🤖⚡ scikit-learn tip #20:\n",
    "\n",
    "New in scikit-learn 0.22: Plot a confusion matrix in one line of code!\n",
    "\n",
    "Highly customizable, including the colormap, display labels, and value formatting.\n",
    "\n",
    "See example 👇"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('http://bit.ly/kaggletrain')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['Pclass', 'Fare']\n",
    "X = df[cols]\n",
    "y = df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pass it a trained model: it makes predictions for X_test and compares them to y_test\n",
    "disp = plot_confusion_matrix(clf, X_test, y_test, cmap='Blues', values_format='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[122,  17],\n",
       "       [ 48,  36]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the \"normal\" confusion matrix\n",
    "disp.confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Want more tips? [View all tips on GitHub](https://github.com/justmarkham/scikit-learn-tips) or [Sign up to receive 5 tips by email every week](https://scikit-learn.tips) 💌\n",
    "\n",
    "© 2020 [Data School](https://www.dataschool.io). All rights reserved."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}