Model Agnostic Explainable Selective Regression via Uncertainty Estimation
This work addresses the need for trustworthy machine learning systems by enabling models to refrain from predicting in regression tasks, which is incremental as it extends selective prediction from classification to regression.
The paper tackles the understudied problem of selective regression by proposing a model-agnostic approach using non-parametric uncertainty estimation, achieving superior performance compared to state-of-the-art methods as demonstrated on 69 datasets.
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.