On some practical challenges of conformal prediction
Abstract
Conformal prediction is a model-free machine learning method for constructing prediction regions at a guaranteed coverage probability level. However, a data scientist often faces three challenges in practice: (i) the determination of a conformal prediction region is only approximate, jeopardizing the finite-sample validity of prediction, (ii) the computation required could be prohibitively expensive, and (iii) the shape of a conformal prediction region is hard to control. This article offers new insights into the relationship among the monotonicity of the non-conformity measure, the monotonicity of the plausibility function, and the exact determination of a conformal prediction region. Based on these new insights, we propose a quadratic-polynomial non-conformity measure that allows a data scientist to circumvent the three challenges simultaneously within the full conformal prediction framework.
Cite
@article{arxiv.2510.10324,
title = {On some practical challenges of conformal prediction},
author = {Liang Hong and Noura Raydan Nasreddine},
journal= {arXiv preprint arXiv:2510.10324},
year = {2026}
}