ridge_regression
import numpy as np
from sklearn.linear_model import Ridge
np.random.seed(1)
X = 2*np.random.rand(100,1)
y = 4 + 3*X + np.random.randn(100,1)
# 正则项系数越大，模型准确率越低，提高了泛化能力
ridge_reg = Ridge(alpha=0.4,solver='sag') # sag随机梯度下降
ridge_reg.fit(X,y)
print(ridge_reg.predict([[1.5]])) # 4+3*1.5=8.39456178，[[8.48214391]]
print(ridge_reg.intercept_) # 截距项 [4.27100296]
print(ridge_reg.coef_) # 其它一些系数 [[2.8074273]]
