Python机器学习：梯度下降法007使用scikit中的随机梯度下降法

#真实数据from sklearn import datasetsfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerboston = datasets.load_boston()X = boston.datay = boston.targetX = X[y < 50]y = y[y < 50]

#拆分数据X_train,X_test,y_train,y_test = train_test_split(X,y)

#归一化standarScaler = StandardScaler()standarScaler.fit(X_train)X_train_standard = standarScaler.transform(X_train)X_test_standard = standarScaler.transform(X_test)

scikit中的SGD

#scikit-learn中的SGDfrom sklearn.linear_model import SGDRegressorsgd_reg = SGDRegressor(max_iter=100)sgd_reg.fit(X_train_standard,y_train)print(sgd_reg.score(X_test_standard,y_test))

打印结果

0.8178403572633133