পদ্ধতির তুলনা করুন

	ম্যাক্রো-গড় F1	মাইক্রো-অ্যাভারেইজড এফ১ (Micro-averaged F1)
ক্ষেত্র	মডেল মূল্যায়ন	মডেল মূল্যায়ন
পরিবার	MCDM	MCDM
উদ্ভবের বছর	2000s	2000s
প্রবর্তক	Multi-class evaluation community	Multi-class evaluation community
ধরন	Evaluation metric	Evaluation metric
মৌলিক উৎস	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.
অপর নাম	Macro F1, Unweighted average F1	Micro F1, Frequency-weighted average F1
সম্পর্কিত	3	4
সারসংক্ষেপ	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.