Порівняння методів

	Макроусереднене F1	Зважена F1
Галузь	Оцінювання моделей	Оцінювання моделей
Родина	MCDM	MCDM
Рік появи	2000s	2000s
Автор методу	Multi-class evaluation community	Multi-class evaluation community
Тип	Evaluation metric	Evaluation metric
Основоположне джерело	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.
Інші назви	Macro F1, Unweighted average F1	Support-weighted F1
Пов'язані	3	3
Підсумок	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.