{"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/catboost-unbiased-boosting-with-categorical","title":"CatBoost: unbiased boosting with categorical features","arxiv_id":"1706.09516","date":"2017-06-28","proceeding":"NeurIPS 2018 12","authors":["Liudmila Prokhorenkova","Gleb Gusev","Aleksandr Vorobev","Anna Veronika Dorogush","Andrey Gulin"],"abstract":"This paper presents the key algorithmic techniques behind CatBoost, a new\ngradient boosting toolkit. Their combination leads to CatBoost outperforming\nother publicly available boosting implementations in terms of quality on a\nvariety of datasets. Two critical algorithmic advances introduced in CatBoost\nare the implementation of ordered boosting, a permutation-driven alternative to\nthe classic algorithm, and an innovative algorithm for processing categorical\nfeatures. Both techniques were created to fight a prediction shift caused by a\nspecial kind of target leakage present in all currently existing\nimplementations of gradient boosting algorithms. In this paper, we provide a\ndetailed analysis of this problem and demonstrate that proposed algorithms\nsolve it effectively, leading to excellent empirical results.","url_abs":"http://arxiv.org/abs/1706.09516v5","url_pdf":"http://arxiv.org/pdf/1706.09516v5.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":"catboost-unbiased-boosting-with-categorical","repo_url":"https://github.com/catboost/catboost","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"catboost-unbiased-boosting-with-categorical","repo_url":"https://github.com/anantgupta129/CatBoost-in-Python-ML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"catboost-unbiased-boosting-with-categorical","repo_url":"https://github.com/hananlibpost/ml_final_project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"catboost-unbiased-boosting-with-categorical","repo_url":"https://github.com/jiangzhongkai/ifly-algorithm_challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"catboost-unbiased-boosting-with-categorical","repo_url":"https://github.com/kazeevn/catboost","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"catboost-unbiased-boosting-with-categorical","repo_url":"https://github.com/sgsonu/SoftBank-Forex-Algorithm-Challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"catboost-unbiased-boosting-with-categorical","repo_url":"https://github.com/sswetank-CS/MIL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"catboost-unbiased-boosting-with-categorical","repo_url":"https://github.com/vj-thakur/catboost","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"catboost-unbiased-boosting-with-categorical","repo_url":"https://github.com/xiadanqing/Binary","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"catboost-unbiased-boosting-with-categorical","repo_url":"https://github.com/yumoh/catboost_iter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1706.09516","atlas_url":"https://app.syntology.ai/?focus=1706.09516","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}