Python machine learning Ridge regression

#岭回归主要是弥补在数据中出现异常值时, improve the stability of linear model, that is, robustness robust

Import Pandas as PD

Import NumPy as NP

Import Matplotlib.pyplot as Plt

From Sklearn import Linear_model

Import Sklearn.metrics as SM

#直接拿最小二乘法数据

Ridgerg=linear_model. Ridge (alpha=0.5,fit_intercept=true,max_iter=10000) #alpha nearer to 0, the more the ridge regression approached the linear regression.

Ridgerg.fit (X_train,y_train) #训练模型

Y_train_pred=ridgerg.predict (X_train) #模型y值

Y_test_pred=ridgerg.predict (x_test) #模型预测y值

ridgerg.coef_# Printing Model Parameters

ridger.intercept_# Intercept Items

Print (' r2= ', Sm.r2_score (y_test,y_test_pred)) #得到R2

#模型超参数的训练

Python machine learning Ridge regression