Should prediction models always deliver a prediction? In the pursuit of
maximum predictive performance, critical considerations of reliability and
fairness are often overshadowed, particularly when it comes to the role of
uncertainty. Selective regression, also known as the "reject option," allows
models to abstain from predictions in cases of considerable uncertainty.
Initially proposed seven decades ago, approaches to selective regression have
mostly focused on distribution-based proxies for measuring uncertainty,
particularly conditional variance. However, this focus neglects the significant
influence of model-specific biases on a model's performance. In this paper, we
propose a novel approach to selective regression by leveraging conformal
prediction, which provides grounded confidence measures for individual
predictions based on model-specific biases. In addition, we propose a
standardized evaluation framework to allow proper comparison of selective
regression approaches. Via an extensive experimental approach, we demonstrate
how our proposed approach, conformalized selective regression, demonstrates an
advantage over multiple state-of-the-art baselines.

通过利用针对模型特定偏差提供基于置信度的个体预测的坚实的置信度度量，我们提出了一种新的选择回归方法，并提出了一个标准化的评估框架，通过广泛的实验方法证明了我们的方法在多个最先进的基准测试中的优势。