Benchmarking non-conformity score functions in conformal prediction

• URL: http://arxiv.org/abs/2605.24983v1
• Date: Sun, 24 May 2026 10:21:42 GMT
• Title: Benchmarking non-conformity score functions in conformal prediction
• Abstract summary: Conformal prediction is a useful and versatile alternative to model calibration in machine learning classification.<n>It replaces single-class prediction with prediction sets.<n>The size and usefulness of the prediction sets relies heavily on the choice of the non-conformity score function.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 \textit{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.

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