방법 비교

	마이크로 평균 F1	매크로 평균 F1
분야	모델 평가	모델 평가
계열	MCDM	MCDM
기원 연도	2000s	2000s
창시자	Multi-class evaluation community	Multi-class evaluation community
유형	Evaluation metric	Evaluation metric
원전	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.
별칭	Micro F1, Frequency-weighted average F1	Macro F1, Unweighted average F1
관련	4	3
요약	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.	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.