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This often occurs in real-world scenarios, like the manufacturing process or medical diagnosis, where diverse patterns in data lead to multiple high-performing solutions. We propose the Rashomon Ensemble, a method that strategically selects models from these diverse high-performing solutions to improve generalization. By grouping models based on both their performance and explanations, we construct ensembles that maximize diversity while maintaining predictive accuracy. This selection ensures that each model covers a distinct region of the solution space, making the ensemble more robust to distribution shifts and variations in unseen data. We validate our approach on both open and proprietary collaborative real-world datasets, demonstrating up to 0.20+ AUROC improvements in scenarios where the Rashomon ratio is large. Additionally, we demonstrate tangible benefits for businesses in various real-world applications, highlighting the robustness, practicality, and effectiveness of our approach.<\/jats:p>","DOI":"10.1145\/3767735","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:17:58Z","timestamp":1758028678000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["\u201cA 6 or a 9?\u201d: Ensemble Learning through the Multiplicity of Performant Models and Explanations"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0429-3280","authenticated-orcid":false,"given":"Gianlucca","family":"Zuin","sequence":"first","affiliation":[{"name":"Universidade Federal de Minas Gerais, Belo Horizonte, Brazil and Instituto Kunumi, Belo Horizonte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9177-4954","authenticated-orcid":false,"given":"Adriano","family":"Veloso","sequence":"additional","affiliation":[{"name":"Universidade Federal de Minas Gerais, Belo Horizonte, Brazil and Instituto Kunumi, Belo Horizonte, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"Comparison of Classifiers in High Dimensional Settings","author":"Aeberhard S.","year":"1992","unstructured":"S. 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