{"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/making-tree-ensembles-interpretable-a","title":"Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach","arxiv_id":"1606.09066","date":"2016-06-29","proceeding":null,"authors":["Satoshi Hara","Kohei Hayashi"],"abstract":"Tree ensembles, such as random forests and boosted trees, are renowned for\ntheir high prediction performance. However, their interpretability is\ncritically limited due to the enormous complexity. In this study, we present a\nmethod to make a complex tree ensemble interpretable by simplifying the model.\nSpecifically, we formalize the simplification of tree ensembles as a model\nselection problem. Given a complex tree ensemble, we aim at obtaining the\nsimplest representation that is essentially equivalent to the original one. To\nthis end, we derive a Bayesian model selection algorithm that optimizes the\nsimplified model while maintaining the prediction performance. Our numerical\nexperiments on several datasets showed that complicated tree ensembles were\nreasonably approximated as interpretable.","url_abs":"http://arxiv.org/abs/1606.09066v3","url_pdf":"http://arxiv.org/pdf/1606.09066v3.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":"making-tree-ensembles-interpretable-a","repo_url":"https://github.com/sato9hara/defragTrees","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.09066","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.09066"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sato9hara/defragTrees","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"80764d0496c8fa77","entry":"argwrapper","repo":"sato9hara/defragTrees","repo_kind":"official","path":"defragTrees.py","file_url":"https://github.com/sato9hara/defragTrees/blob/HEAD/defragTrees.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"80764d0496c8fa77"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}