Nea*_*bfi 3 python scikit-learn cross-validation grid-search data-science
在scikit-learn 0.20之前,我们可以result.grid_scores_[result.best_index_]用来获取标准偏差。(它返回为例:mean: 0.76172, std: 0.05225, params: {'n_neighbors': 21})
scikit学习0.20以获得最佳分数的标准偏差的最佳方法是什么?
在较新的版本中,将grid_scores_重命名为cv_results_。根据文档,您需要:
Run Code Online (Sandbox Code Playgroud)best_index_ : int The index (of the cv_results_ arrays) which corresponds to the best > candidate parameter setting. The dict at search.cv_results_['params'][search.best_index_] gives the > parameter setting for the best model, that gives the highest mean score (search.best_score_).
因此,您需要
result.cv_results_['params'][result.best_index_]或result.best_params_ 最佳平均得分:- result.cv_results_['mean_test_score'][result.best_index_]或result.best_score_
最佳标准:- result.cv_results_['std_test_score'][result.best_index_]
| 归档时间: |
|
| 查看次数: |
1409 次 |
| 最近记录: |