building-conformal-prediction-set

To generate calibrated prediction intervals with finite-sample coverage, use split-conformal prediction by applying a nonconformity score to a held-out calibration split and computing a corrected conformal quantile. This provides documented empirical coverage and interval width for your regression outputs.

Yes, conformal prediction creates valid prediction sets for black-box classification models. By evaluating a nonconformity score on disjoint calibration data, you compute a conformal quantile that yields finite-sample marginal coverage without needing access to the model's internal architecture.

To quantify uncertainty for heteroscedastic regression targets, apply conformalized quantile regression. This workflow uses disjoint train, calibration, and test splits to compute a finite-sample-corrected conformal quantile, reporting empirical coverage alongside average interval width.

Yes, split conformal prediction requires disjoint train, calibration, and test splits to maintain data exchangeability. The Skill flags exchangeability problems and refuses to compute a valid conformal quantile if your data split structure is invalid.

Your conformal prediction coverage guarantee fails when exchangeability is violated by overlapping data splits or incorrect nonconformity scoring. The Skill enforces strict separation of train, calibration, and test sets and flags invalid splits to ensure empirical coverage holds.

Split conformal prediction for uncertainty quantification requires exchangeability and disjoint data splits, reducing available training data. It provides marginal, not conditional, coverage guarantees and requires explicit empirical coverage and set-size reporting to validate results.