विधियों की तुलना करें

	एफ-बीटा स्कोर	मैक्रो-औसत F1
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM
उद्भव वर्ष	1979	2000s
प्रवर्तक	C. J. van Rijsbergen	Multi-class evaluation community
प्रकार	Evaluation metric	Evaluation metric
मौलिक स्रोत	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.	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	F-measure with parameter beta	Macro F1, Unweighted average F1
संबंधित	5	3
सारांश	The F-beta score is a weighted harmonic mean of precision and recall that allows customizing the relative importance of recall versus precision through a parameter beta. It generalizes the F1-score, which is the special case where beta = 1.	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.