Conformal calibrators
Machine Learning
2019-02-19 v1 Machine Learning
Abstract
Most existing examples of full conformal predictive systems, split-conformal predictive systems, and cross-conformal predictive systems impose severe restrictions on the adaptation of predictive distributions to the test object at hand. In this paper we develop split-conformal and cross-conformal predictive systems that are fully adaptive. Our method consists in calibrating existing predictive systems; the input predictive system is not supposed to satisfy any properties of validity, whereas the output predictive system is guaranteed to be calibrated in probability. It is interesting that the method may also work without the IID assumption, standard in conformal prediction.
Cite
@article{arxiv.1902.06579,
title = {Conformal calibrators},
author = {Vladimir Vovk and Ivan Petej and Paolo Toccaceli and Alex Gammerman},
journal= {arXiv preprint arXiv:1902.06579},
year = {2019}
}
Comments
10 pages, 2 figures