{"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/dart-dropouts-meet-multiple-additive","title":"DART: Dropouts meet Multiple Additive Regression Trees","arxiv_id":"1505.01866","date":"2015-05-07","proceeding":null,"authors":["K. V. Rashmi","Ran Gilad-Bachrach"],"abstract":"Multiple Additive Regression Trees (MART), an ensemble model of boosted\nregression trees, is known to deliver high prediction accuracy for diverse\ntasks, and it is widely used in practice. However, it suffers an issue which we\ncall over-specialization, wherein trees added at later iterations tend to\nimpact the prediction of only a few instances, and make negligible contribution\ntowards the remaining instances. This negatively affects the performance of the\nmodel on unseen data, and also makes the model over-sensitive to the\ncontributions of the few, initially added tress. We show that the commonly used\ntool to address this issue, that of shrinkage, alleviates the problem only to a\ncertain extent and the fundamental issue of over-specialization still remains.\nIn this work, we explore a different approach to address the problem that of\nemploying dropouts, a tool that has been recently proposed in the context of\nlearning deep neural networks. We propose a novel way of employing dropouts in\nMART, resulting in the DART algorithm. We evaluate DART on ranking, regression\nand classification tasks, using large scale, publicly available datasets, and\nshow that DART outperforms MART in each of the tasks, with a significant\nmargin. We also show that DART overcomes the issue of over-specialization to a\nconsiderable extent.","url_abs":"http://arxiv.org/abs/1505.01866v1","url_pdf":"http://arxiv.org/pdf/1505.01866v1.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":"dart-dropouts-meet-multiple-additive","repo_url":"https://github.com/mavillan/yolanda","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1505.01866","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}