{"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/safepredict-a-meta-algorithm-for-machine","title":"SafePredict: A Meta-Algorithm for Machine Learning That Uses Refusals to Guarantee Correctness","arxiv_id":"1708.06425","date":"2017-08-21","proceeding":null,"authors":["Mustafa A. Kocak","David Ramirez","Elza Erkip","Dennis E. Shasha"],"abstract":"SafePredict is a novel meta-algorithm that works with any base prediction\nalgorithm for online data to guarantee an arbitrarily chosen correctness rate,\n$1-\\epsilon$, by allowing refusals. Allowing refusals means that the\nmeta-algorithm may refuse to emit a prediction produced by the base algorithm\non occasion so that the error rate on non-refused predictions does not exceed\n$\\epsilon$. The SafePredict error bound does not rely on any assumptions on the\ndata distribution or the base predictor. When the base predictor happens not to\nexceed the target error rate $\\epsilon$, SafePredict refuses only a finite\nnumber of times. When the error rate of the base predictor changes through time\nSafePredict makes use of a weight-shifting heuristic that adapts to these\nchanges without knowing when the changes occur yet still maintains the\ncorrectness guarantee. Empirical results show that (i) SafePredict compares\nfavorably with state-of-the art confidence based refusal mechanisms which fail\nto offer robust error guarantees; and (ii) combining SafePredict with such\nrefusal mechanisms can in many cases further reduce the number of refusals. Our\nsoftware (currently in Python) is included in the supplementary material.","url_abs":"http://arxiv.org/abs/1708.06425v2","url_pdf":"http://arxiv.org/pdf/1708.06425v2.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":"safepredict-a-meta-algorithm-for-machine","repo_url":"https://github.com/ans682/SafePredict_and_Forecasting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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}