Võrdle meetodeid

	Kaalutud F1	Makrokeskmine F1
Valdkond	Mudelite hindamine	Mudelite hindamine
Perekond	MCDM	MCDM
Tekkeaasta	2000s	2000s
Looja	Multi-class evaluation community	Multi-class evaluation community
Tüüp	Evaluation metric	Evaluation metric
Algallikas	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.
Rööpnimetused	Support-weighted F1	Macro F1, Unweighted average F1
Seotud	3	3
Kokkuvõte	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.	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.