Porovnať metódy

	Makro-priemerované F1	Vážený F1
Odbor	Hodnotenie modelov	Hodnotenie modelov
Rodina	MCDM	MCDM
Rok vzniku	2000s	2000s
Tvorca	Multi-class evaluation community	Multi-class evaluation community
Typ	Evaluation metric	Evaluation metric
Pôvodný zdroj	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.
Ďalšie názvy	Macro F1, Unweighted average F1	Support-weighted F1
Príbuzné	3	3
Zhrnutie	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.