【机器学习】--线性回归中L1正则和L2正则

# 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))

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))

# elastic_net函数
import numpy as np
from sklearn.linear_model import ElasticNet
from sklearn.linear_model import SGDRegressor


X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)
#两种方式实现Elastic_net
elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)
elastic_net.fit(X, y)
print(elastic_net.predict(1.5))

sgd_reg = SGDRegressor(penalty='elasticnet')
sgd_reg.fit(X, y.ravel())
print(sgd_reg.predict(1.5))