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)
吴裕雄 python 机器学习——线性回归模型的相关教程结束。
