{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/interpreting-tree-ensembles-with-intrees","title":"Interpreting Tree Ensembles with inTrees","arxiv_id":"1408.5456","date":"2014-08-23","proceeding":null,"authors":["Houtao Deng"],"abstract":"Tree ensembles such as random forests and boosted trees are accurate but\ndifficult to understand, debug and deploy. In this work, we provide the inTrees\n(interpretable trees) framework that extracts, measures, prunes and selects\nrules from a tree ensemble, and calculates frequent variable interactions. An\nrule-based learner, referred to as the simplified tree ensemble learner (STEL),\ncan also be formed and used for future prediction. The inTrees framework can\napplied to both classification and regression problems, and is applicable to\nmany types of tree ensembles, e.g., random forests, regularized random forests,\nand boosted trees. We implemented the inTrees algorithms in the \"inTrees\" R\npackage.","url_abs":"http://arxiv.org/abs/1408.5456v1","url_pdf":"http://arxiv.org/pdf/1408.5456v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"interpreting-tree-ensembles-with-intrees","repo_url":"https://github.com/IBCNServices/GENESIM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"future-prediction","task_name":"Future prediction"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1408.5456","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}