Unverified50% confidenceFactExact time
The evaluation metric for intent classification is usually accuracy or macro-averaged F1, because class distributions are often imbalanced.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
200-Message Classification Test: Clef-Flash Needs Only 4 Requests, Precision on Par with qwen3.5-9b
redditr/deeplearning10/15/2026
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UnverifiedIn a binary classification confusion matrix, the F1 score is the harmonic mean of precision and recall.72% similarUnverifiedIn 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-ROC70% 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.69% similarUnverifiedThe average rubric score describes how many criteria were satisfied, not how many workflows were completed perfectly.67% similarUnverifiedMacro-F1 calculates the F1 score for each category separately and then takes the arithmetic mean, without considering differences in sample sizes across categories, which better reflects the model's performance on minority categories.67% similar
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