99.9% accuracy can mean zero discoveries: the class imbalance trap in scientific ML
bravo_abad · x · 2026-09-17
A vivid explainer on why accuracy misleads in rare-event scientific problems: with only 10 positives in 10,000 samples, a classifier that always predicts negative scores 99.9% accuracy while finding none of the events that matter. In settings like new-physics detection, cancer screening, or molecule hunting, recall and precision are the questions that count — a model can be less accurate overall yet far more scientifically useful. Metrics should follow the scientific objective, not the reverse.
Related event: Scholar Warns Accuracy Can Be Misleading Under Class Imbalance(2 posts)→
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