resampling

Imbalance turns up in fraud, churn, and abuse, and the naive answer (oversample, done) leaks data and inflates offline metrics. The signal is favoring cost-sensitive learning, fixing the metric, and resampling correctly. Here is the full toolkit with its tradeoffs.

Resampling, reweighting, and focal loss tackle class imbalance from different angles, and each carries a real downside. The signal is matching the method to the model and metric, not blindly oversampling. Here is the breakdown.