Beyond calibration: estimating the grouping loss of modern neural networks

Good decision making requires machine-learning models to provide trustworthy confidence scores. To this end, recent work has focused on miscalibration, i.e, the over or under confidence of model scores. Yet, contrary to widespread belief, calibration is not enough: even a classifier with the best possible accuracy and perfect calibration can have confidence scores far from the true posterior probabilities. This is due to the grouping loss, created by samples with the same confidence scores but different true posterior probabilities. Proper scoring rule theory shows that given the calibration loss, the missing piece to characterize individual errors is the grouping loss. While there are many estimators of the calibration loss, none exists for the grouping loss in standard settings. Here, we propose an estimator to approximate the grouping loss. We use it to study modern neural network architectures in vision and NLP. We find that the grouping loss varies markedly across architectures, and that it is a key model-comparison factor across the most accurate, calibrated, models. We also show that distribution shifts lead to high grouping loss.