Calibration is the process of mapping a model's raw scores or outputs to probabilities or class memberships that better reflect observed outcomes. A calibrated classifier's predicted probabilities should correspond closely to real-world frequencies, improving interpretability and decision thresholds.
Calibration is the process of mapping a raw score onto a class membership, for binary and multiclass classification. Some ML.NET trainers have a NonCalibrated suffix. These algorithms produce a raw score that then must be mapped to a class probability.