方法对比

	宏平均 F1	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	Macro F1, Unweighted average F1	Support-weighted F1
相关	3	3
摘要	Macro-averaged F1 computes the F1-score independently for each class and then takes the unweighted arithmetic mean. It treats all classes equally, regardless of their frequency in the dataset, making it useful for imbalanced multi-class problems.	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.