{"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/reweighting-with-boosted-decision-trees","title":"Reweighting with Boosted Decision Trees","arxiv_id":"1608.05806","date":"2016-08-20","proceeding":null,"authors":["A. Rogozhnikov"],"abstract":"Machine learning tools are commonly used in modern high energy physics (HEP)\nexperiments. Different models, such as boosted decision trees (BDT) and\nartificial neural networks (ANN), are widely used in analyses and even in the\nsoftware triggers.\n  In most cases, these are classification models used to select the \"signal\"\nevents from data. Monte Carlo simulated events typically take part in training\nof these models. While the results of the simulation are expected to be close\nto real data, in practical cases there is notable disagreement between\nsimulated and observed data. In order to use available simulation in training,\ncorrections must be introduced to generated data. One common approach is\nreweighting - assigning weights to the simulated events. We present a novel\nmethod of event reweighting based on boosted decision trees. The problem of\nchecking the quality of reweighting step in analyses is also discussed.","url_abs":"http://arxiv.org/abs/1608.05806v1","url_pdf":"http://arxiv.org/pdf/1608.05806v1.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":"reweighting-with-boosted-decision-trees","repo_url":"https://github.com/UF-HH/bbbbAnalysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"reweighting-with-boosted-decision-trees","repo_url":"https://github.com/philippgadow/reweight_samples","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"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}