Probability Calibration › Disadvantages

Calibration requires held-out data that are representative of the population where the probabilities will be used. If the calibration set is small, the fitted mapping is noisy, especially in the tails. Isotonic recalibration has flexible monotone fitted values at the observed scores; our predictor interpolates between those scores. With few observations, it can overfit the calibration sample and produce unstable probabilities on new cases. The two-parameter sigmoid limits flexibility, which can help with a smaller sample, but it can underfit complex patterns and still be poorly estimated.

A strictly increasing recalibration map preserves score order and ROC-AUC. Isotonic maps can create ties, which can change AUC substantially in either direction. An unconstrained sigmoid can also reverse order if its fitted slope is negative. These mappings use only the original score; they cannot recover feature information that score has discarded. That mapping leaves the base fit and its training labels unchanged. With clean, representative calibration labels, it can still correct some probability-scale distortions caused by label noise or a nonrepresentative training sample.

Calibration is also population-specific. A mapping that is valid on one hospital, region, customer segment, or time period may be invalid on another. Under distribution shift, the relationship between predicted probability and outcome can change, and a previously calibrated model can become miscalibrated. For these reasons, calibration should be checked on untouched data, across subgroups, and over time rather than assumed to hold permanently.