← 📊 Evaluation & ML Foundations
Core
Handling Imbalanced Data
On a 99-to-1 class split, a model that predicts the majority class scores 99% accuracy and catches nothing, which is why accuracy lies on imbalanced data. The fixes are resampling (SMOTE, undersampling), class weights, and threshold moving, judged on PR-AUC and recall-at-precision, not accuracy. The biggest pitfall is resampling before you split.
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