Venn-Abers predictors
Machine Learning
2014-06-24 v2 Machine Learning
Abstract
This paper continues study, both theoretical and empirical, of the method of Venn prediction, concentrating on binary prediction problems. Venn predictors produce probability-type predictions for the labels of test objects which are guaranteed to be well calibrated under the standard assumption that the observations are generated independently from the same distribution. We give a simple formalization and proof of this property. We also introduce Venn-Abers predictors, a new class of Venn predictors based on the idea of isotonic regression, and report promising empirical results both for Venn-Abers predictors and for their more computationally efficient simplified version.
Keywords
Cite
@article{arxiv.1211.0025,
title = {Venn-Abers predictors},
author = {Vladimir Vovk and Ivan Petej},
journal= {arXiv preprint arXiv:1211.0025},
year = {2014}
}
Comments
18 pages; to appear in the UAI 2014 Proceedings