{"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/to-tune-or-not-to-tune-the-number-of-trees-in","title":"To tune or not to tune the number of trees in random forest?","arxiv_id":"1705.05654","date":"2017-05-16","proceeding":null,"authors":["Philipp Probst","Anne-Laure Boulesteix"],"abstract":"The number of trees T in the random forest (RF) algorithm for supervised\nlearning has to be set by the user. It is controversial whether T should simply\nbe set to the largest computationally manageable value or whether a smaller T\nmay in some cases be better. While the principle underlying bagging is that\n\"more trees are better\", in practice the classification error rate sometimes\nreaches a minimum before increasing again for increasing number of trees. The\ngoal of this paper is four-fold: (i) providing theoretical results showing that\nthe expected error rate may be a non-monotonous function of the number of trees\nand explaining under which circumstances this happens; (ii) providing\ntheoretical results showing that such non-monotonous patterns cannot be\nobserved for other performance measures such as the Brier score and the\nlogarithmic loss (for classification) and the mean squared error (for\nregression); (iii) illustrating the extent of the problem through an\napplication to a large number (n = 306) of datasets from the public database\nOpenML; (iv) finally arguing in favor of setting it to a computationally\nfeasible large number, depending on convergence properties of the desired\nperformance measure.","url_abs":"http://arxiv.org/abs/1705.05654v1","url_pdf":"http://arxiv.org/pdf/1705.05654v1.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":"to-tune-or-not-to-tune-the-number-of-trees-in","repo_url":"https://github.com/PhilippPro/OOBCurve","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1705.05654","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}