Handle a skewed-class problem correctly with the right resampling, weighting, threshold, and metric choices.
## CONTEXT Imbalanced classification (fraud, churn, rare disease) breaks naive workflows: accuracy looks great while the minority class is ignored, and careless oversampling leaks data across the validation boundary. Handling it well combines metric choice, class weighting or resampling done inside the CV loop, and…
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