Classical ML
Calibration
The property that predicted probabilities match observed frequencies - among all cases scored 0.7, about 70% are positive.
Why interviewers ask about it
Independent of discrimination: a model can have AUC 1.0 and be badly miscalibrated. Essential whenever a score feeds an expected-value decision. Fix with Platt, isotonic or temperature scaling on held-out data.
Related terms
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