Calibration

Calibration is the process of adjusting the outputs of a machine learning model to reflect true probabilities. This is particularly important in classification tasks where the confidence scores produced by the model may not correspond accurately to the actual likelihood of an event occurring. A well-calibrated model will provide probability estimates that are reliable and can be interpreted meaningfully. Common techniques for calibration include Platt scaling and isotonic regression, and calibration is often evaluated using reliability diagrams and Brier scores. Proper calibration is crucial in applications such as medical diagnosis, risk assessment, and any domain where decision-making is based on probability estimates.