Calibration (in a binary context) basically means that the confidence of a model/score matches the probability that a particular label is positive or not. For

Calibration (in a binary context) basically means that the confidence of a model/score matches the probability that a particular label is positive or not. For instance, a calibrated classifier for a coin flip predictor should output 50-50. A poorly calibrated classifier would output higher confidence for heads/tails.