With the wide adoption of machine learning techniques, requirements have evolved beyond sheer high performance, often requiring models to be trustworthy. A common approach to increase the trustworthiness of such systems is to allow them to refrain from predicting. Such a framework is known as selective prediction. While selective prediction for classification tasks has been widely analyzed, the problem of selective regression is understudied. This paper presents a novel approach to selective regression that utilizes model-agnostic non-parametric uncertainty estimation. Our proposed framework showcases superior performance compared to state-of-the-art selective regressors, as demonstrated through comprehensive benchmarking on 69 datasets. Finally, we use explainable AI techniques to gain an understanding of the drivers behind selective regression. We implement our selective regression method in the open-source Python package doubt and release the code used to reproduce our experiments.

翻译：随着机器学习技术的广泛采用，相关需求已从单纯追求高性能发展到要求模型具有可信赖性。增强此类系统可信度的常见方法是允许其放弃预测，这种框架被称为选择性预测。尽管分类任务中的选择性预测已被广泛研究，但选择性回归问题却鲜有探讨。本文提出了一种利用模型无关非参数不确定性估计的选择性回归新方法。通过在69个数据集上的综合基准测试，我们提出的框架展现出比现有最优选择性回归器更优越的性能。最后，我们运用可解释人工智能技术来理解选择性回归背后的驱动因素。我们已将所提出的选择性回归方法实现于开源Python包doubt中，并公开了用于复现实验的代码。