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Unfortunately, interpretable classification models, such as linear, rule-based, and decision tree models, are superseded by more accurate but complex learning paradigms, such as deep neural networks and ensemble methods. More specifically, for tabular data classification, models based on tree ensembles, such as random forest or XGBoost, are still competitive compared to deep learning ones and are often preferred to the latter. However, they share the same interpretability issues, due to the complexity of the learned model and, consequently, offer low explainability of predictions. Existing solutions consist of computing some feature importance score or extracting an approximate surrogate model from the learned tree ensemble. However, these methods lead to surrogate models with either poor fidelity or questionable comprehensibility. In this paper, we propose to improve this trade-off using Goodman-Kruskal\u2019s association measure to find groups of instances with predictions that are explained by shared groups of features. To build this structure, instances are first described by SHAP values, which capture local feature importance, and then co-clustered with features on the basis of these SHAP values. Next, a surrogate model is built as a set of shallow decision trees learned for the different groups of instances and subsets of relevant features. Our experiments show that our method produces surrogate models that explain random forest and XGBoost classifiers with competitive fidelity and higher comprehensibility compared to recent state-of-the-art competitors.<\/jats:p>","DOI":"10.1007\/s10994-025-06932-9","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T20:09:39Z","timestamp":1763669379000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Explaining Random Forest and XGBoost with Shallow Decision Trees by Co-clustering Feature Importance"],"prefix":"10.1007","volume":"114","author":[{"given":"Ruggero G.","family":"Pensa","sequence":"first","affiliation":[]},{"given":"Anton","family":"Crombach","sequence":"additional","affiliation":[]},{"given":"Sergio","family":"Peignier","sequence":"additional","affiliation":[]},{"given":"Christophe","family":"Rigotti","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"6932_CR1","doi-asserted-by":"crossref","unstructured":"Arik, S. \u00d6., & Pfister, T. 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Pensa has received a grant from Inria under its visiting scholar program. This work was also partially funded by BQR INSA Lyon 2023 Neurinfo, ANR project C2R-IA ANR-22-CE56-0005, PEPR Sant\u00e9 Num\u00e9rique project 22-PESN-0002, Fondation ARC grant ARCPJA22020060002212, and Institut National du Cancer grant PLBIO22-071.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"R.G. Pensa is member of the Editorial Board. The authors have no further Conflict of interest to declare that are relevant to the content of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare that this research did not require Ethics approval or Consent to participate since it does not concern human participants or human or animal datasets.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"The authors of this manuscript consent to its publication.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"All data are available online and accessible to everyone.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of Data and Material"}},{"value":"Source code and scripts used in our experiments are available at\n                      \n                      .","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Availability"}}],"article-number":"287"}}