This paper introduces Target Strangeness, a novel difficulty estimator for conformal prediction (CP) that offers an alternative approach for normalizing prediction intervals (PIs). By assessing how atypical a prediction is within the context of its nearest neighbours' target distribution, Target Strangeness can surpass the current state-of-the-art performance. This novel difficulty estimator is evaluated against others in the context of several conformal regression experiments.
@article{arxiv.2410.19077,
title = {Target Strangeness: A Novel Conformal Prediction Difficulty Estimator},
author = {Alexis Bose and Jonathan Ethier and Paul Guinand},
journal= {arXiv preprint arXiv:2410.19077},
year = {2024}
}