{"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/on-the-relationship-between-data-efficiency","title":"On the Relationship between Data Efficiency and Error for Uncertainty Sampling","arxiv_id":"1806.06123","date":"2018-06-15","proceeding":"ICML 2018 7","authors":["Stephen Mussmann","Percy Liang"],"abstract":"While active learning offers potential cost savings, the actual data\nefficiency---the reduction in amount of labeled data needed to obtain the same\nerror rate---observed in practice is mixed. This paper poses a basic question:\nwhen is active learning actually helpful? We provide an answer for logistic\nregression with the popular active learning algorithm, uncertainty sampling.\nEmpirically, on 21 datasets from OpenML, we find a strong inverse correlation\nbetween data efficiency and the error rate of the final classifier.\nTheoretically, we show that for a variant of uncertainty sampling, the\nasymptotic data efficiency is within a constant factor of the inverse error\nrate of the limiting classifier.","url_abs":"http://arxiv.org/abs/1806.06123v1","url_pdf":"http://arxiv.org/pdf/1806.06123v1.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":"on-the-relationship-between-data-efficiency","repo_url":"https://worksheets.codalab.org/worksheets/0x8ef22fd3cd384029bf1d1cae5b268f2d","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.06123","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}