import numpy as np from sklearn import datasets,linear_model from sklearn.model_selection import train_test_split def load_data(): diabetes = datasets.load_diabetes() return train_test_split(diabetes.data,diabetes.target,test_size=0.25,random_state=0) #线性回归模型 def test_LinearRegression(*data): X_train,X_test,y_train,y_test=data regr = linear_model.LinearRegression() regr.fit(X_train,y_train) print('Coefficients:%s, intercept %.2f' % (regr.coef_, regr.intercept_)) print("Residual sum of squares: %.2f"% np.mean((regr.predict(X_test) - y_test) ** 2)) print('Score: %.2f' % regr.score(X_test, y_test)) # 产生用于回归问题的数据集 X_train,X_test,y_train,y_test=load_data() # 调用 test_LinearRegression test_LinearRegression(X_train,X_test,y_train,y_test)