Compara mètodes

	F1-score mitjà macro	F1 micro-mitjana
Camp	Avaluació de models	Avaluació de models
Família	MCDM	MCDM
Any d'origen	2000s	2000s
Autor original	Multi-class evaluation community	Multi-class evaluation community
Tipus	Evaluation metric	Evaluation metric
Font seminal	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.
Àlies	Macro F1, Unweighted average F1	Micro F1, Frequency-weighted average F1
Relacionats	3	4
Resum	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.	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.