Common
Conformal prediction
Statistical technique for producing prediction sets
Conformal prediction
Conformal prediction (CP) is an algorithm for uncertainty quantification that produces statistically valid prediction regions (multidimensional prediction intervals) for any underlying point predictor (whether statistical, machine learning, or deep learning) 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. Conformal prediction requires a user-specified significance level for which the algorithm should produce its predictions. This significance level restricts the frequency of errors that the algorithm is allowed to make. For example, a significance level of 0.1 means that the algorithm can make at...
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