A calibration layer is a post-prediction adjustment that modifies model scores or probabilities so they better match observed label distributions. It is often used to correct prediction bias, improve probability estimates, and align model outputs with empirical outcomes.
A post-prediction adjustment, typically to account for prediction bias. The adjusted predictions and probabilities should match the distribution of an observed set of labels.