Benchmarking non-conformity score functions in conformal prediction

Abstract:Conformal prediction is a useful and versatile alternative to model calibration in machine learning classification. It replaces single-class prediction with prediction sets, guaranteeing that the a priori probability of the prediction sets containing the true class is larger than or equal to a pre-specified rate. The size and usefulness of the prediction sets relies heavily on the choice of the non-conformity score function. The scientific literature contains many examples of non-conformity score functions but there is an absence of studies examining their properties and effectiveness. In this paper, we give an overview of properties of non-conformity score functions. We give examples of non-conformity score functions in the existing literature and introduce original modifications. We introduce an original method of evaluating the prediction set sizes of conformal predictors and use it to provide a comparison between non-conformity score functions. We also examine efficacy of different non-conformity score functions for class-conditional conformal prediction in a setting with imbalanced classes.

Machine Learning (cs.LG)

arXiv:2605.24983 [cs.LG]

(or arXiv:2605.24983v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.24983 Focus to learn more arXiv-issued DOI via DataCite

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