{"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/cost-complexity-pruning-of-random-forests","title":"Cost-complexity pruning of random forests","arxiv_id":"1703.05430","date":"2017-03-15","proceeding":null,"authors":["Kiran Bangalore Ravi","Jean Serra"],"abstract":"Random forests perform bootstrap-aggregation by sampling the training samples\nwith replacement. This enables the evaluation of out-of-bag error which serves\nas a internal cross-validation mechanism. Our motivation lies in using the\nunsampled training samples to improve each decision tree in the ensemble. We\nstudy the effect of using the out-of-bag samples to improve the generalization\nerror first of the decision trees and second the random forest by post-pruning.\nA preliminary empirical study on four UCI repository datasets show consistent\ndecrease in the size of the forests without considerable loss in accuracy.","url_abs":"http://arxiv.org/abs/1703.05430v2","url_pdf":"http://arxiv.org/pdf/1703.05430v2.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":"cost-complexity-pruning-of-random-forests","repo_url":"https://github.com/beedotkiran/randomforestpruning-ismm-2017","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}