sklearn集成了大多數模型評價指標，這可以很大程度上方便我們的使用，尤其在對進行進行自動調參時可以方便我們進行選擇。
做下這個筆記主要是為了補充之前的內容：sklearn快速入門教程：（四）模型自動調參

后續如果有時間可能會把具體的指標公式全部轉過來，方便查看。

ScoringFunctionComment

分類指標
‘accuracy’	metrics.accuracy_score
‘balanced_accuracy’	metrics.balanced_accuracy_score
‘average_precision’	metrics.average_precision_score
‘neg_brier_score’	metrics.brier_score_loss
‘f1’	metrics.f1_score	for binary targets
‘f1_micro’	metrics.f1_score	micro-averaged
‘f1_macro’	metrics.f1_score	macro-averaged
‘f1_weighted’	metrics.f1_score	weighted average
‘f1_samples’	metrics.f1_score	by multilabel sample
‘neg_log_loss’	metrics.log_loss	requires predict_proba support
‘precision’ etc.	metrics.precision_score	suffixes apply as with ‘f1’
‘recall’ etc.	metrics.recall_score	suffixes apply as with ‘f1’
‘jaccard’ etc.	metrics.jaccard_score	suffixes apply as with ‘f1’
‘roc_auc’	metrics.roc_auc_score
‘roc_auc_ovr’	metrics.roc_auc_score
‘roc_auc_ovo’	metrics.roc_auc_score
‘roc_auc_ovr_weighted’	metrics.roc_auc_score
‘roc_auc_ovo_weighted’	metrics.roc_auc_score
聚類指標
‘adjusted_mutual_info_score’	metrics.adjusted_mutual_info_score
‘adjusted_rand_score’	metrics.adjusted_rand_score
‘completeness_score’	metrics.completeness_score
‘fowlkes_mallows_score’	metrics.fowlkes_mallows_score
‘homogeneity_score’	metrics.homogeneity_score
‘mutual_info_score’	metrics.mutual_info_score
‘normalized_mutual_info_score’	metrics.normalized_mutual_info_score
‘v_measure_score’	metrics.v_measure_score
回歸指標
‘explained_variance’	metrics.explained_variance_score
‘max_error’	metrics.max_error
‘neg_mean_absolute_error’	metrics.mean_absolute_error
‘neg_mean_squared_error’	metrics.mean_squared_error
‘neg_root_mean_squared_error’	metrics.mean_squared_error
‘neg_mean_squared_log_error’	metrics.mean_squared_log_error
‘neg_median_absolute_error’	metrics.median_absolute_error
‘r2’	metrics.r2_score
‘neg_mean_poisson_deviance’	metrics.mean_poisson_deviance
‘neg_mean_gamma_deviance’	metrics.mean_gamma_deviance

總結

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