Compară metode

	F1 mediu-macro	F1 mediată la nivel micro
Domeniu	Evaluarea modelelor	Evaluarea modelelor
Familie	MCDM	MCDM
Anul apariției	2000s	2000s
Autorul original	Multi-class evaluation community	Multi-class evaluation community
Tip	Evaluation metric	Evaluation metric
Sursa seminală	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.
Denumiri alternative	Macro F1, Unweighted average F1	Micro F1, Frequency-weighted average F1
Înrudite	3	4
Rezumat	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.