Linganisha mbinu

	F1 ya wastani wa makro	F1-yenye-uzito
Nyanja	Tathmini ya Modeli	Tathmini ya Modeli
Familia	MCDM	MCDM
Mwaka wa asili	2000s	2000s
Mwanzilishi	Multi-class evaluation community	Multi-class evaluation community
Aina	Evaluation metric	Evaluation metric
Chanzo asilia	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.
Majina mbadala	Macro F1, Unweighted average F1	Support-weighted F1
Zinazohusiana	3	3
Muhtasari	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.