When Offline Selectors Cannot Beat the Best Single Model: A Diagnostic Study on edX Dropout Prediction

ArXi:2606.04161v1 Announce Type: new Different predictors often excel on different inputs, so picking the best one per instance promises higher accuracy than committing to a single model. In practice, selectors trained from logged data routinely fail to beat the strongest single predictor. Three causes typically go unseparated before tuning is applied: a mismatched learner, a state that does not predict which model wins, or buffer-to-deployment label shift. A three-stage diagnostic rules them out on a shared buffer.