विधियों की तुलना करें

	मैक्रो-औसत F1	माइक्रो-औसत F1
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM
उद्भव वर्ष	2000s	2000s
प्रवर्तक	Multi-class evaluation community	Multi-class evaluation community
प्रकार	Evaluation metric	Evaluation metric
मौलिक स्रोत	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.
उपनाम	Macro F1, Unweighted average F1	Micro F1, Frequency-weighted average F1
संबंधित	3	4
सारांश	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.