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Macro averaging is a commonly used metric aggregation method in multi-class evaluation: precision, recall, and F1 score are first calculated separately for each class, and then the results for all classes are arithmetically averaged, with each class weighted equally.
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10/14/2026
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UnverifiedmAP (mean average precision) is calculated as follows: plot precision-recall curves under different IoU criteria, compute the area under each curve (AP), and then average over all classes.68% similarUnverifiedAn evaluation set (eval set) is a carefully constructed collection of input samples and their corresponding expected outputs or evaluation criteria, used to systematically measure a model's performance on specific tasks.67% similarUnverifiedCommon evaluation metrics for classification models include accuracy, precision, recall, F1 score, and the ROC-AUC curve65% similarUnverifiedStratified sampling draws samples according to the proportion of each class in the population, ensuring that the class distribution of the subset is consistent with the original dataset65% similarUnverifiedThe mainstream industry evaluation approach is to build a three-component set of "evaluation set + evaluation metrics + evaluation model," where the evaluation model is typically another strong model that scores automatically, supplemented by manual spot checks.64% similar
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