Model Agnostic Explainable Selective Regression via Uncertainty
  Estimation
Andrea Pugnana Carlos Mougan Dan Saattrup Nielsen
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.