השוואת שיטות

	F1 בממוצע-מיקרו	F1 משוקלל
תחום	הערכת מודלים	הערכת מודלים
משפחה	MCDM	MCDM
שנת המקור	2000s	2000s
הוגה השיטה	Multi-class evaluation community	Multi-class evaluation community
סוג	Evaluation metric	Evaluation metric
מקור מכונן	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.
כינויים	Micro F1, Frequency-weighted average F1	Support-weighted F1
קשורות	4	3
תקציר	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.	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.