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Evaluating Model Retraining under Drift: Paired Comparisons of Cumulative Subgroup Disparity
Choosing when to retrain a deployed classifier requires assessing subgroup error rates across the sequence of models used, including periods between updates. We compare complete scheduled, loss-triggered, and subgroup-gap-triggered policies with retaining the initial model on the same observations a
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