Conformal Prediction

The conformal prediction module provides distribution-free uncertainty quantification with finite-sample coverage guarantees.

Classification Methods

inductive_conformal(model, calib_loader, alpha)	Classical Inductive Conformal Prediction (ICP) — Vovk, Gammerman, and Shafer, Algorithmic Learning in a Random World (2005).
mondrian_conformal(model, calib_loader, alpha)	Mondrian Conformal Prediction — Papadopoulos, Reliable Classification with Conformal Predictors (2008).
aps(model, calib_loader, alpha)	Adaptive Prediction Sets (APS) — Romano, Patterson, and Candes, NeurIPS 2020.
raps(model, calib_loader, alpha[, lam, k_reg])	Regularized APS (RAPS) — Tsesmelis et al., ICML 2021.

Regression Methods

jackknife_plus(model_fn, train_dataset, alpha)	Jackknife+ Intervals — Barber, Candès, and Ramdas, *Ann.
cv_plus(model_fn, train_dataset, folds, alpha)	Cross-Validation+ Intervals (CV+) — Barber et al., JASA 2021.
conformalized_quantile_regression(...[, ...])	Conformalized Quantile Regression (CQR) — Romano, Patterson, and Candes, NeurIPS 2019.

Metrics

empirical_coverage(y, sets)	Fraction of test examples where y_i ∈ Ŝ_i.
average_set_size(sets)
conditional_coverage(y, sets, groups)	Coverage conditioned on groups (e.g., class labels).

Visualization