Stacked conformal prediction
Machine Learning
2026-03-31 v3 Machine Learning
Abstract
We consider a method for conformalizing a stacked ensemble of predictive models, showing that the potentially simple form of the meta-learner at the top of the stack enables a procedure with manageable computational cost that achieves approximate marginal validity without requiring the use of a separate calibration sample. Empirical results indicate that the method compares favorably to a standard inductive alternative.
Cite
@article{arxiv.2505.12578,
title = {Stacked conformal prediction},
author = {Paulo C. Marques F},
journal= {arXiv preprint arXiv:2505.12578},
year = {2026}
}
Comments
12 pages, 2 figures