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线性回归

import matplotlib.pyplot as pltimport numpy as npfrom sklearn import datasets, linear_modelfrom sklearn.metrics import mean_squared_error, r2_score# Load the diabetes datasetdiabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)# Use only one featurediabetes_X = diabetes_X[:, np.newaxis, 2]# 将数据分割成训练/测试集diabetes_X_train = diabetes_X[:-20]#从后面第20个开始往前数diabetes_X_test = diabetes_X[-20:]#从后面第20个开始往后数# 将目标拆分为训练/测试集diabetes_y_train = diabetes_y[:-20]diabetes_y_test = diabetes_y[-20:]# 建立线性回归的对象regr = linear_model.LinearRegression()# 使用训练集训练模型regr.fit(diabetes_X_train, diabetes_y_train)# 使用测试集进行预测diabetes_y_pred = regr.predict(diabetes_X_test)# The coefficientsprint('Coefficients: \n', regr.coef_)# The mean squared errorprint('Mean squared error: %.2f'      % mean_squared_error(diabetes_y_test, diabetes_y_pred))#mean_squared_error是均方误差回归损失# 决定系数:1是完美的预测print('Coefficient of determination: %.2f'% r2_score(diabetes_y_test, diabetes_y_pred))# Plot outputsplt.scatter(diabetes_X_test, diabetes_y_test,color='black')plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)plt.xticks(())plt.yticks(())plt.show()

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