Unverified50% confidenceFactExact time
Macro-average accuracy first computes the accuracy of each class separately and then takes the arithmetic mean, making it more sensitive to the performance of minority classes; overall accuracy is the proportion of all samples predicted correctly and is easily affected by the class sample size distribution.
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Long-term
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10/27/2026
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Related Claims
UnverifiedIn imbalanced classification problems, a model can achieve very high accuracy even by predicting all samples as the majority class, so it is necessary to shift to more discriminative metrics such as precision-recall curves, F1 score, Matthews correlation coefficient, or AUC-ROC77% similarUnverifiedPrecision refers to the proportion of samples the model identifies as positive that are truly positive; recall refers to the proportion of all true positives that the model successfully identifies; the two typically have an inverse relationship.77% similarUnverifiedAverage accuracy masks distributional skew; a judge that gets all easy cases right and is overconfident on hard cases can post a high average score while failing at critical points.76% similarUnverifiedOn data with extremely imbalanced classes, accuracy rewards a judge for agreeing on the easy majority class, whereas Cohen's kappa corrects for the possibility of chance agreement, so kappa should be used rather than accuracy.75% similarUnverifiedThe evaluation metric for intent classification is usually accuracy or macro-averaged F1, because class distributions are often imbalanced.74% similar
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