方法对比

	微平均F1分数	加权F1
领域	模型评估	模型评估
方法族	MCDM	MCDM
起源年份	2000s	2000s
提出者	Multi-class evaluation community	Multi-class evaluation community
类型	Evaluation metric	Evaluation metric
开创性文献	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.	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.
别名	Micro F1, Frequency-weighted average F1	Support-weighted F1
相关	4	3
摘要	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.	Weighted F1 computes the F1-score for each class and then takes a weighted average, where weights are proportional to the number of samples in each class (support). It provides a middle ground between macro and micro-averaging.