Conformal prediction

Statistical technique for producing prediction sets

Conformal prediction (CP) is a machine learning framework for uncertainty quantification that produces statistically valid prediction regions for any underlying point predictor only assuming exchangeability of the data. CP works by computing nonconformity scores on previously labeled data, and using these to create prediction sets on a new (unlabeled) test data point. A transductive version of CP was first proposed in 1998 by Gammerman, Vovk, and Vapnik, and since, several variants of conformal prediction have been developed with different computational complexities, formal guarantees, and practical applications.

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