Lasso通过GridSearchCV: ConvergenceWarning: Objective没有收敛
Lasso通过GridSearchCV: ConvergenceWarning: Objective没有收敛
提问于 2022-09-19 13:57:23
我试图找出拉索回归的最佳参数：
alpha_tune = {'alpha': np.linspace(start=0.000005, stop=0.02, num=200)}
model_tuner = Lasso(fit_intercept=True)
cross_validation = RepeatedKFold(n_splits=5, n_repeats=3, random_state=1)
model = GridSearchCV(estimator=model_tuner, param_grid=alpha_tune, cv=cross_validation, scoring='neg_mean_squared_error', n_jobs=-1).fit(features_train_std, labels_train)
print(model.best_params_['alpha'])我的变量被贬低和标准化了。但我得到了以下错误：
ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.279e+02, tolerance: 6.395e-01我知道这个错误已经被报告过好几次了，但是之前的文章都没有回答如何解决这个问题。在我的例子中，错误是由下限0.000005非常小这一事实产生的，但是这是一个合理的值，正如通过信息标准解决调优问题所表明的那样：
lasso_aic = LassoLarsIC(criterion='aic', fit_intercept=True, eps=1e-16, normalize=False)
lasso_aic.fit(X_train_std, y_train)
print('Lambda: {:.8f}'.format(lasso_aic.alpha_))
lasso_bic = LassoLarsIC(criterion='bic', fit_intercept=True, eps=1e-16, normalize=False)
lasso_bic.fit(X_train_std, y_train)
print('Lambda: {:.8f}'.format(lasso_bic.alpha_))AIC和BIC的价值约为0.000008。如何解决这一警告呢？
回答 1
Stack Overflow用户
回答已采纳
发布于 2022-09-20 10:17:00
在Lasso中增加默认参数max_iter=1000将完成以下工作：
alpha_tune = {'alpha': np.linspace(start=0.000005, stop=0.02, num=200)}
model_tuner = Lasso(fit_intercept=True, max_iter=5000)
cross_validation = RepeatedKFold(n_splits=5, n_repeats=3, random_state=1)
model = GridSearchCV(estimator=model_tuner, param_grid=alpha_tune, cv=cross_validation, scoring='neg_mean_squared_error', n_jobs=-1).fit(features_train_std, labels_train)
print(model.best_params_['alpha'])页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/73774565
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