Enhancing Conformal Prediction Using E-Test Statistics
Machine Learning
2024-03-29 v1 Artificial Intelligence
Statistics Theory
Statistics Theory
Abstract
Conformal Prediction (CP) serves as a robust framework that quantifies uncertainty in predictions made by Machine Learning (ML) models. Unlike traditional point predictors, CP generates statistically valid prediction regions, also known as prediction intervals, based on the assumption of data exchangeability. Typically, the construction of conformal predictions hinges on p-values. This paper, however, ventures down an alternative path, harnessing the power of e-test statistics to augment the efficacy of conformal predictions by introducing a BB-predictor (bounded from the below predictor).
Cite
@article{arxiv.2403.19082,
title = {Enhancing Conformal Prediction Using E-Test Statistics},
author = {A. A. Balinsky and A. D. Balinsky},
journal= {arXiv preprint arXiv:2403.19082},
year = {2024}
}