@inproceedings{
traub2024overcoming,
title={Overcoming Common Flaws in the Evaluation of Selective Classification Systems},
author={Jeremias Traub and Till J. Bungert and Carsten T. L{\"u}th and Michael Baumgartner and Klaus Maier-Hein and Lena Maier-hein and Paul F Jaeger},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=2TktDpGqNM}
}
Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenarios such as clinical diagnostics. While current evaluation of these systems typically assumes fixed working points based on pre-defined rejection thresholds, methodological progress requires benchmarking the general performance of systems akin to the $\mathrm{AUROC}$ in standard classification. In this work, we define 5 requirements for multi-threshold metrics in selective classification regarding task alignment, interpretability, and flexibility, and show how current approaches fail to meet them. We propose the Area under the Generalized Risk Coverage curve ($\mathrm{AUGRC}$), which meets all requirements and can be directly interpreted as the average risk of undetected failures. We empirically demonstrate the relevance of $\mathrm{AUGRC}$ on a comprehensive benchmark spanning 6 data sets and 13 confidence scoring functions. We find that the proposed metric substantially changes metric rankings on 5 out of the 6 data sets.
Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Jeremias Traub Till J. Bungert Carsten T. Lüth Michael Baumgartner Klaus Maier-Hein Lena Maier-hein Paul F Jaeger