套索回归目标不收敛

Question

这是我的密码。当我运行它时，山脊很好，但是对于套索，我得到错误消息：

ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations.

请帮忙

from sklearn.linear_model import LinearRegression, Lasso, Ridge, RidgeCV, LassoCV
from sklearn.model_selection import cross_val_score
import numpy as np
import sys

dataset = np.loadtxt(sys.argv[1], delimiter = ',')
X = dataset[:,:10]
y = dataset[:,10]

ridge_cv = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1, 10, 100]).fit(X,y)

lasso_cv = LassoCV(alphas=[1e-3, 1e-2, 1e-1, 1, 10, 100]).fit(X,y)

lin_reg = LinearRegression()
ridge_reg = Ridge(alpha = ridge_cv.alpha_)
lasso_reg = Lasso(alpha = lasso_cv.alpha_)

print(cross_val_score(lin_reg, X, y, cv=2).mean())
print(cross_val_score(ridge_reg, X, y, cv=2).mean())
print(cross_val_score(lasso_reg, X, y, cv=2).mean())

Answer 1

增加公差。

检查documentation优化的容差：如果更新小于tol，优化代码将检查双间隙的最佳性，并继续执行，直到它小于tol

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