Conformal Prediction

2 articles

Artificial Intelligence 17 Sep 2026 6 min read

Conformal Prediction Sets Need Exchangeable Calibration Data

A classifier that emits 0.93 for one class does not automatically provide a statistical statement that the class is correct with probability 0.93. Conformal prediction takes a different route: it uses held-out labeled examples to construct a set of candidate labels with a target marginal coverage level. For split conformal classification, the base model can remain fixed. The guarantee comes from ranking a test example’s conformity or nonconformity score against scores computed on an exchangeable calibration sample. That assumption is the part that gives the coverage statement its scope.

Artificial Intelligence Conformal Prediction

Artificial Intelligence 11 Sep 2026 10 min read

Build Prediction Sets with Conformal Prediction

Build Prediction Sets with Conformal Prediction A classifier usually returns one label even when several labels are plausible. That is convenient for software interfaces, but it can hide uncertainty exactly where mistakes are expensive. A document router might be unsure between billing and account, yet an argmax still emits one of them. Conformal prediction offers another interface: return a set of labels sized according to the evidence. Easy inputs can produce one label. Ambiguous inputs can produce several. Under specific assumptions, the procedure also gives a finite-sample coverage guarantee.

Artificial Intelligence Model Evaluation