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 StandardScalerdef 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()
