  ML models deployed in production often have to face unknown domain changes,
fundamentally different from their training settings. Performance prediction
models carry out the crucial task of measuring the impact of these changes on
model performance. We study the generalization capabilities of various
performance prediction models to new domains by learning on generated synthetic
perturbations. Empirical validation on a benchmark of ten tabular datasets
shows that models based upon state-of-the-art shift detection metrics are not
expressive enough to generalize to unseen domains, while Error Predictors bring
a consistent improvement in performance prediction under shift. We additionally
propose a natural and effortless uncertainty estimation of the predicted
accuracy that ensures reliable use of performance predictors. Our
implementation is available at https:
//github.com/dataiku-research/performance_prediction_under_shift.
