plot_ols-cell13.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 938.23786125]\n",
      "Mean squared error: 2548.07\n",
      "Variance score: 0.47\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfwZ6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f41dba69400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The coefficients\n",
    "print('Coefficients: \\n', regr.coef_)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')\n",
    "plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n",
    "\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}