{"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/understanding-concept-drift","title":"Understanding Concept Drift","arxiv_id":"1704.00362","date":"2017-04-02","proceeding":null,"authors":["Geoffrey I. Webb","Loong Kuan Lee","François Petitjean","Bart Goethals"],"abstract":"Concept drift is a major issue that greatly affects the accuracy and\nreliability of many real-world applications of machine learning. We argue that\nto tackle concept drift it is important to develop the capacity to describe and\nanalyze it. We propose tools for this purpose, arguing for the importance of\nquantitative descriptions of drift in marginal distributions. We present\nquantitative drift analysis techniques along with methods for communicating\ntheir results. We demonstrate their effectiveness by application to three\nreal-world learning tasks.","url_abs":"http://arxiv.org/abs/1704.00362v1","url_pdf":"http://arxiv.org/pdf/1704.00362v1.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":"understanding-concept-drift","repo_url":"https://github.com/LeeLoongKuan/DataAnalysisR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}