Salīdzināt metodes

	Mikro vidējais F1 rādītājs	Precizitāte
Nozare	Modeļu novērtēšana	Modeļu novērtēšana
Saime	MCDM	MCDM
Izcelsmes gads	2000s	20th century
Autors	Multi-class evaluation community	Historical statistical foundations
Tips	Evaluation metric	Evaluation metric
Pirmavots	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.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
Citi nosaukumi	Micro F1, Frequency-weighted average F1	Overall Accuracy, Correct Classification Rate
Saistītās	4	5
Kopsavilkums	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.	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.