{"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/hyperparameters-and-tuning-strategies-for","title":"Hyperparameters and Tuning Strategies for Random Forest","arxiv_id":"1804.03515","date":"2018-04-10","proceeding":null,"authors":["Philipp Probst","Marvin Wright","Anne-Laure Boulesteix"],"abstract":"The random forest algorithm (RF) has several hyperparameters that have to be\nset by the user, e.g., the number of observations drawn randomly for each tree\nand whether they are drawn with or without replacement, the number of variables\ndrawn randomly for each split, the splitting rule, the minimum number of\nsamples that a node must contain and the number of trees. In this paper, we\nfirst provide a literature review on the parameters' influence on the\nprediction performance and on variable importance measures.\n  It is well known that in most cases RF works reasonably well with the default\nvalues of the hyperparameters specified in software packages. Nevertheless,\ntuning the hyperparameters can improve the performance of RF. In the second\npart of this paper, after a brief overview of tuning strategies we demonstrate\nthe application of one of the most established tuning strategies, model-based\noptimization (MBO). To make it easier to use, we provide the tuneRanger R\npackage that tunes RF with MBO automatically. In a benchmark study on several\ndatasets, we compare the prediction performance and runtime of tuneRanger with\nother tuning implementations in R and RF with default hyperparameters.","url_abs":"http://arxiv.org/abs/1804.03515v2","url_pdf":"http://arxiv.org/pdf/1804.03515v2.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":"hyperparameters-and-tuning-strategies-for","repo_url":"https://github.com/PhilippPro/tuneRanger","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.03515","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}