L1,L2正则化代码# L1正则
import numpy as np
from sklearn.linear_model import Lasso
from sklearn.linear_model import SGDRegressor
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)
lasso_reg = Lasso(alpha=0.15)
lasso_reg.fit(X, y)
print(lasso_reg.predict(1.5))正则化回归算法
sgd_reg = SGDRegressor(penalty='l1')
sgd_reg.fit(X, y.ravel())
print(sgd_reg.predict(1.5))
# L2正则
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.linear_model import SGDRegressor
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)
#两种⽅式第⼀种岭回归
ridge_reg = Ridge(alpha=1, solver='auto')
ridge_reg.fit(X, y)
print(ridge_reg.predict(1.5))#预测1.5的值
#第⼆种使⽤随机梯度下降中L2正则
sgd_reg = SGDRegressor(penalty='l2')
sgd_reg.fit(X, y.ravel())
print(sgd_reg.predict(1.5))
1# elastic_net函数
2import numpy as np
3from sklearn.linear_model import ElasticNet
4from sklearn.linear_model import SGDRegressor
5
6
7 X = 2 * np.random.rand(100, 1)
8 y = 4 + 3 * X + np.random.randn(100, 1)
9#两种⽅式实现Elastic_net
10 elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)
11 elastic_net.fit(X, y)
12print(elastic_net.predict(1.5))
13
14 sgd_reg = SGDRegressor(penalty='elasticnet')
15 sgd_reg.fit(X, y.ravel())
16print(sgd_reg.predict(1.5))

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