機器學習_ML_模型評估

原文連結
在使用GridViewCV或是cross_val_score做模型評估的時候，總是會需要我們做scoring的設定，好做效能指標!(另一篇混洧矩陣也記得閱讀)

各種的數值計算都跟上面這張圖有關。(取自(維基百科))

SCIKIT-LEARN

下面的表格取自SCIKIT-LEARN，已經有幫我們做好各種類型演算法的模型評估的LIB了。
配上上圖的說明，可以很快速的了解各種模型評估的數值來源。

Scoring	Function	Comment
Classification
accuracy	accuracy_score
average_precision	average_precision_score
f1	f1_score	for binary targets
f1_micro	f1_score	micro-averaged
f1_macro	f1_score	macro-averaged
f1_weighted	f1_score	weighted average
f1_samples	f1_score	by multilabel sample
neg_log_loss	log_loss	requires predict_proba support
precision etc.	precision_score	suffixes apply as with f1
recall etc.	recall_score	suffixes apply as with f1
roc_auc	roc_auc_score
Clustering
adjusted_mutual_info_score	adjusted_mutual_info_score
adjusted_rand_score	adjusted_rand_score
completeness_score	completeness_score
fowlkes_mallows_score	fowlkes_mallows_score
homogeneity_score	homogeneity_score
mutual_info_score	mutual_info_score
normalized_mutual_info_score	normalized_mutual_info_score
v_measure_score	v_measure_score
Regression
explained_variance	explained_variance_score
neg_mean_absolute_error	mean_absolute_error
neg_mean_squared_error	mean_squared_error
neg_mean_squared_log_error	mean_squared_log_error
neg_median_absolute_error	median_absolute_error
r2	r2_score