{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:11Z","timestamp":1761176171685,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>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, leading to underperformance or, conversely, unusually high performance in certain subgroups (e.g., sex=female \u2227 marital_status=married). Identifying such subgroups can support practical decisions on which subpopulation a model is safe to deploy or where more training data is required. 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.<\/jats:p>","DOI":"10.3233\/faia250998","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:48:13Z","timestamp":1761126493000},"source":"Crossref","is-referenced-by-count":0,"title":["SubROC: AUC-Based Discovery of Exceptional Subgroup Performance for Binary Classifiers"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2292-1188","authenticated-orcid":false,"given":"Tom","family":"Siegl","sequence":"first","affiliation":[{"name":"University of Rostock, 18055 Rostock, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7680-3182","authenticated-orcid":false,"given":"Kutalm\u0131\u015f","family":"Co\u015fkun","sequence":"additional","affiliation":[{"name":"University of Rostock, 18055 Rostock, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9371-1702","authenticated-orcid":false,"given":"Bjarne C.","family":"Hiller","sequence":"additional","affiliation":[{"name":"University of Rostock, 18055 Rostock, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5210-7956","authenticated-orcid":false,"given":"Amin","family":"Mirzaei","sequence":"additional","affiliation":[{"name":"University of Rostock, 18055 Rostock, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7620-1376","authenticated-orcid":false,"given":"Florian","family":"Lemmerich","sequence":"additional","affiliation":[{"name":"University of Passau, 94032 Passau, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4296-3481","authenticated-orcid":false,"given":"Martin","family":"Becker","sequence":"additional","affiliation":[{"name":"University of Rostock, 18055 Rostock, Germany"},{"name":"Hessian AI \/ Marburg University, 35037 Marburg, Germany"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250998","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:48:13Z","timestamp":1761126493000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250998"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250998","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}