mhjensen
114 lines
9.7 KiB
Plaintext
114 lines
9.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Coefficients: \n",
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" [ 945.4992184]\n",
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"Mean squared error: 3471.92\n",
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"Variance score: 0.41\n"
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]
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},
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{
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"data": {
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"image/png": 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"text/plain": [
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"<matplotlib.figure.Figure at 0x1a1045f6d8>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn import datasets, linear_model\n",
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"from sklearn.metrics import mean_squared_error, r2_score\n",
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"\n",
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"diabetes = datasets.load_diabetes()\n",
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"diabetes_X = diabetes.data[:, np.newaxis, 2]\n",
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"diabetes_X_train = diabetes_X[:-50]\n",
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"diabetes_X_test = diabetes_X[-50:]\n",
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"\n",
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"#print (diabetes_X.shape)\n",
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"\n",
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"# Split the targets into training/testing sets\n",
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"diabetes_y_train = diabetes.target[:-50]\n",
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"diabetes_y_test = diabetes.target[-50:]\n",
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"\n",
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"# Create linear regression object\n",
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"regr = linear_model.LinearRegression()\n",
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"\n",
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"# Train the model using the training sets\n",
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"regr.fit(diabetes_X_train, diabetes_y_train)\n",
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"\n",
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"# Make predictions using the testing set\n",
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"diabetes_y_pred = regr.predict(diabetes_X_test)\n",
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"\n",
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"# The coefficients\n",
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"print('Coefficients: \\n', regr.coef_)\n",
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"# The mean squared error\n",
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"print(\"Mean squared error: %.2f\"\n",
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" % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n",
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"# Explained variance score: 1 is perfect prediction\n",
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"print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n",
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"\n",
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"# Plot outputs\n",
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"plt.scatter(diabetes_X_test, diabetes_y_test, color='black')\n",
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"plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n",
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"\n",
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"plt.xticks(())\n",
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"plt.yticks(())\n",
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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