Calibration

Calibration in the context of predictive modeling refers to the degree to which predicted probabilities align with actual outcomes. A well-calibrated model will output probabilities that reflect the true likelihood of an event occurring. Mathematically, calibration can be assessed using reliability diagrams, where predicted probabilities are plotted against observed frequencies, and metrics such as the Brier score or log loss can be employed to quantify calibration accuracy. Calibration is particularly important in probabilistic models, as it ensures that the probabilities assigned to predictions are meaningful and can be interpreted correctly. Techniques such as Platt scaling or isotonic regression are often used to improve model calibration, particularly in classification tasks where probability estimates are critical for decision-making.