线性回归

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets,linear_model
from sklearn.metrics import mean_squared_error,r2_score
#加载数据
diabetes=datasets.load_diabetes()
data_X=diabetes.data[:,np.newaxis,2]#选取数据的维度
#将其分为训练与测试
diabetes_X_train=data_X[:-20]
diabetes_X_test=data_X[-20:]
diabetes_Y_train=diabetes.target[:-20]
diabetes_Y_test=diabetes.target[-20:]
#创造线性回归对像
regr=linear_model.LinearRegression()
regr.fit(diabetes_X_train,diabetes_Y_train)
diabetes_Y_pred=regr.predict(diabetes_X_test)
plt.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()
print('Cofficients:\n',regr.coef_)