calibratedset

Calibratedset refers to a subset of data, predictions, or measurements that has undergone calibration against a reference standard. The term is used across disciplines including metrology, statistics, and machine learning, and its exact meaning depends on context.

In metrology and instrumentation, a calibrated set of measurements is produced by applying a calibration function or correction derived from reference artifacts, standards, or known true values. Calibration addresses systematic errors, biases, and instrument drift, producing values that better reflect true quantities.

In probabilistic forecasting and machine learning, a calibrated set of probabilities or forecast outputs is post-processed so that their reported frequencies match observed frequencies. After calibration, if an event is predicted with probability p, among all instances with that prediction, the event occurs about p proportion of the time. Common calibration methods include isotonic regression, Platt scaling, temperature scaling, and beta calibration.

Calibration quality is evaluated with reliability in reliability diagrams, the Brier score, and calibration error metrics. A well-calibrated set balances calibration with sharpness, aiming for precise yet honest probabilities.

In practice, calibrated sets enable better decision making, risk assessment, and model evaluation by ensuring reported quantities reflect true likelihoods or true measurements.

See also: calibration, conformal prediction, reliability diagram, isotonic regression, probability calibration.