Comparer des méthodes

	F1-score moyenné par micro-classe	Exactitude
Domaine	Évaluation de modèles	Évaluation de modèles
Famille	MCDM	MCDM
Année d'origine	2000s	20th century
Auteur d'origine	Multi-class evaluation community	Historical statistical foundations
Type	Evaluation metric	Evaluation metric
Source fondatrice	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.
Alias	Micro F1, Frequency-weighted average F1	Overall Accuracy, Correct Classification Rate
Apparentées	4	5
Résumé	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.