Machine learning (ML) is increasingly employed in real-world applications like medicine or economics, thus, potentially affecting large populations. However, ML models often do not perform homogeneously across such populations resulting in subgroups of the population (e.g., sex=female AND marital_status=married) where the model underperforms or, conversely, is particularly accurate. Identifying and describing such subgroups can support practical decisions on which subpopulation a model is safe to deploy or where more training data is required. The potential of identifying and analyzing such subgroups has been recognized, however, an efficient and coherent framework for effective search is missing. Consequently, we introduce SubROC, an open-source, easy-to-use framework based on Exceptional Model Mining for reliably and efficiently finding strengths and weaknesses of classification models in the form of interpretable population subgroups. SubROC incorporates common evaluation measures (ROC and PR AUC), efficient search space pruning for fast exhaustive subgroup search, control for class imbalance, adjustment for redundant patterns, and significance testing. We illustrate the practical benefits of SubROC in case studies as well as in comparative analyses across multiple datasets.
View on arXiv@article{siegl2025_2505.11283,
title={ SubROC: AUC-Based Discovery of Exceptional Subgroup Performance for Binary Classifiers },
author={ Tom Siegl and Kutalmış Coşkun and Bjarne Hiller and Amin Mirzaei and Florian Lemmerich and Martin Becker },
journal={arXiv preprint arXiv:2505.11283},
year={ 2025 }
}