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Discretized conformal prediction for efficient distribution‐free inference
Author(s) -
Chen Wenyu,
Chun KelliJean,
Barber Rina Foygel
Publication year - 2018
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.173
Subject(s) - conformal map , discretization , inference , grid , regression analysis , computer science , regression , mathematics , algorithm , mathematical optimization , artificial intelligence , statistics , machine learning , geometry , mathematical analysis
In regression problems where there is no known true underlying model, conformal prediction methods enable prediction intervals to be constructed without any assumptions on the distribution of the underlying data, except that the training and test data are assumed to be exchangeable. However, these methods bear a heavy computational cost—and, to be carried out exactly, the regression algorithm would need to be fitted infinitely many times. In practice, the conformal prediction method is run by simply considering only a finite grid of finely spaced values for the response variable. This paper develops discretized conformal prediction algorithms that are guaranteed to cover the target value with the desired probability and that offer a trade‐off between computational cost and prediction accuracy. Copyright © 2018 John Wiley & Sons, Ltd.