手法を比較

	マイクロ平均F1 (Micro-averaged F1)	精度
分野	モデル評価	モデル評価
系統	MCDM	MCDM
提唱年	2000s	20th century
提唱者	Multi-class evaluation community	Historical statistical foundations
種類	Evaluation metric	Evaluation metric
原典	Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
別名	Micro F1, Frequency-weighted average F1	Overall Accuracy, Correct Classification Rate
関連	4	5
概要	Micro-averaged F1 computes the F1-score by aggregating true positives, false positives, and false negatives across all classes, then calculating a single metric. It is equivalent to accuracy in multi-class classification and is useful when class distributions reflect their natural importance.	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.