{"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/infiniteboost-building-infinite-ensembles","title":"InfiniteBoost: building infinite ensembles with gradient descent","arxiv_id":"1706.01109","date":"2017-06-04","proceeding":null,"authors":["Alex Rogozhnikov","Tatiana Likhomanenko"],"abstract":"In machine learning ensemble methods have demonstrated high accuracy for the\nvariety of problems in different areas. Two notable ensemble methods widely\nused in practice are gradient boosting and random forests. In this paper we\npresent InfiniteBoost - a novel algorithm, which combines important properties\nof these two approaches. The algorithm constructs the ensemble of trees for\nwhich two properties hold: trees of the ensemble incorporate the mistakes done\nby others; at the same time the ensemble could contain the infinite number of\ntrees without the over-fitting effect. The proposed algorithm is evaluated on\nthe regression, classification, and ranking tasks using large scale, publicly\navailable datasets.","url_abs":"http://arxiv.org/abs/1706.01109v2","url_pdf":"http://arxiv.org/pdf/1706.01109v2.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":"infiniteboost-building-infinite-ensembles","repo_url":"https://github.com/arogozhnikov/infiniteboost","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"regression-1","task_name":"regression"}],"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}