岭回归自动调参

import pandas as pd
import numpy as np
from sklearn import model_selection
from sklearn.linear_model import Ridge,RidgeCV
from sklearn.metrics import mean_squared_error

#model=Ridge(alpha=10e-6,fit_intercept=True)
#构造不同的lambda值
Lambdas=np.logspace(-5,2,200)
#设置交叉验证的参数，使用均方误差评估
ridge_cv=RidgeCV(alphas=Lambdas,scoring=‘neg_mean_squared_error’,cv=20)
ridge_cv.fit(x_train,y_train)

#基于最佳lambda值建模
ridge=Ridge(alpha=ridge_cv.alpha_)
ridge.fit(x_train,y_train)

#model.fit(x_train, y_train)

#save model
##joblib.dump(model, path+r’/model/linear_model.pkl’)
#load model
#model_= joblib.load(path+r’/model/linear_model.pkl’)

pred_y = ridge.predict(x_test)