我正在使用 ElasticNet 优化模型，但出现了一些奇怪的行为。当我用一个小值设置公差超参数时，我得到

"ConvergenceWarning: Objective did not converge" 

错误。所以我尝试了更大的容差值，收敛误差消失了，但现在测试数据始终给出更高的均方根误差值。这对我来说似乎倒退了，如果模型不收敛，是什么导致它给出更好的 RMSE 分数，甚至给出一致的分数？我在 gridsearch 函数中使用它：

没有错误，分数差：

GridSearchCV(cv=KFold(n_splits=4, random_state=None, shuffle=True),
             estimator=ElasticNet(),
             param_grid={'alpha': [0.01], 'fit_intercept': [True],
                         'l1_ratio': [0.75, 0.8, 0.85, 0.9],
                         'max_iter': range(350, 450, 50), 'normalize': [False],
                         'random_state': [3], 'selection': ['random'],
                         'tol': [0.1]},
             refit='nmse', return_train_score=True,
             scoring={'mae': 'neg_mean_absolute_error',
                      'nmse': 'neg_mean_squared_error',
                      'nmsle': 'neg_mean_squared_log_error', 'r2': 'r2'})

Running GridSearchCV:
C:\miniconda3\envs\tf\lib\site-packages\sklearn\linear_model\_coordinate_descent.py:529: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1253.5072980364057, tolerance: 848.4484859258542
  positive)

错误，好分数：

GridSearchCV(cv=KFold(n_splits=4, random_state=None, shuffle=True),
             estimator=ElasticNet(),
             param_grid={'alpha': [0.01], 'fit_intercept': [True],
                         'l1_ratio': [0.75, 0.8, 0.85, 0.9],
                         'max_iter': range(350, 450, 50), 'normalize': [False],
                         'random_state': [3], 'selection': ['random'],
                         'tol': [0.01]},
             refit='nmse', return_train_score=True,
             scoring={'mae': 'neg_mean_absolute_error',
                      'nmse': 'neg_mean_squared_error',
                      'nmsle': 'neg_mean_squared_log_error', 'r2': 'r2'})