sk-learn实现L2岭回归，对线性回归正则化

岭回归算法：

from sklearn.datasets import load_boston
from sklearn.externals import joblib
from sklearn.linear_model import Ridge, RidgeCV
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler



def liner_ridge():
    '''
    岭回归
    :return: 
    '''

    #1.获取数据
    data = load_boston()

    #2.数据集划分
    x_train,x_test,y_train,y_test = train_test_split(data.data,data.target,random_state=20)

    #3.特征工程-标准化
    transfer = StandardScaler()
    x_train = transfer.fit_transform(x_train)
    x_test = transfer.fit_transform(x_test)

    #4.机器学习-线性回归（岭回归）
    # estimator = Ridge(alpha = 1)
    # estimator = RidgeCV(alphas=(0.1,1,8,5,11))
    # estimator.fit(x_train,y_train)
    #
    # #模型保存
    # joblib.dump(estimator,"./data/test.pkl")

    estimator = joblib.load("./data/test.pkl")

    #5.模型评估
    #获取系数等值
    y_predict = estimator.predict(x_test)
    print("预测值为：",y_predict)
    print("模型中的系数为：",estimator.coef_)
    print("模型中的偏执为：",estimator.intercept_)
    print(estimator.alpha_)
    print(estimator.alphas)
    #评价模型 均方误差
    error = mean_squared_error(y_test,y_predict)
    print("误差为：",error)


if __name__ == '__main__':
    liner_ridge()