学习笔记：sklearn-线性回归

import numpy as np
import pandas as pd
from sklearn.linear_model import Ridge, LinearRegression, Lasso
from sklearn.model_selection import train_test_split 
from sklearn.datasets import fetch_california_housing as fch
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_score

housevalue=fch()

X=pd.DataFrame(housevalue.data)
y=housevalue.target
X.columns=housevalue.feature_names

X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.3)

for i in [X_train, X_test]:
    i.index=range(i.shape[0])

linear=LinearRegression()
cross_val_score(linear, X_train, y_train, cv=10).mean()

#岭回归
reg=Ridge().fit(X_train, y_train)
reg.score(X_test, y_test)

lasso=Lasso(alpha=0.1).fit(X_train, y_train)
lasso.score(X_test,y_test)