线性回归算法学习

from sklearn import linear_modelfrom sklearn import datasetsfrom sklearn.model_selection import cross_val_predictimport matplotlib.pyplot as plt# 线性回归算法lr = linear_model.LinearRegression()print(lr)# 引入算法的数据boston = datasets.load_boston()print(boston)y = boston.targetprint(y)# 交叉验证预测predicted = cross_val_predict(lr,boston.data,y,cv=10)print(predicted)# 得到画布对象和轴对象fig, ax = plt.subplots()print(fig)print(ax)# 更具数据画散点图及颜色ax.scatter(y, predicted, edgecolors=(0, 0, 1))# 画出分界线ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)plt.show()