sklearn正规方程和梯度下降

一。sklearnAPI

 1 from sklearn.datasets import load_boston
 2 from sklearn.model_selection import train_test_split
 3 from sklearn.preprocessing import StandardScaler
 4 from sklearn.linear_model import LinearRegression,SGDRegressor #正规方程，梯度下降
 5 from sklearn.metrics import mean_squared_error #均方误差
 6 def linear1():
 7     """
 8     正规方程对波士顿房价进行预测
 9     :return:
10     """
11     #1.导入数据
12     boston=load_boston()
13     #print(boston)
14     #2.划分数据集
15     x_train,x_test,y_train,y_test=train_test_split(boston.data,boston.target,random_state=22)
16     #3.特征工程：标准化
17     transfer=StandardScaler()
18     x_train=transfer.fit_transform(x_train)
19     x_test=transfer.transform(x_test)
20     #4.预估器
21     estimator=LinearRegression()
22     estimator.fit(x_train,y_train)\
23     #5.得出模型
24     print("正规方程权重系数：\n",estimator.coef_)
25     print("正规方程偏置：\n",estimator.intercept_)
26     #6.模型评估
27     y_predict=estimator.predict(x_test)
28     error = mean_squared_error(y_test,y_predict)
29     print("正规方程均方误差：\n：",error)
30 
31 def linear2():
32     """
33     梯度下降对波士顿房价进行预测
34     :return:
35     """
36     #1.导入数据
37     boston=load_boston()
38     #print(boston)
39     #2.划分数据集
40     x_train,x_test,y_train,y_test=train_test_split(boston.data,boston.target,random_state=22)
41     #3.特征工程：标准化
42     transfer=StandardScaler()
43     x_train=transfer.fit_transform(x_train)
44     x_test=transfer.transform(x_test)
45     #4.预估器
46     estimator=SGDRegressor(learning_rate="constant",eta0=0.001,max_iter=10000)
47     estimator.fit(x_train,y_train)
48     #5.得出模型
49     print("梯度下降权重系数：\n",estimator.coef_)
50     print("梯度下降偏置：\n",estimator.intercept_)
51     #6.模型评估
52     y_predict=estimator.predict(x_test)
53     error = mean_squared_error(y_test,y_predict)
54     print("梯度下降均方误差：\n：",error)
55 if __name__ == "__main__":
56     linear1()
57     linear2()