{"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/fisher-consistency-for-prior-probability","title":"Fisher consistency for prior probability shift","arxiv_id":"1701.05512","date":"2017-01-19","proceeding":null,"authors":["Dirk Tasche"],"abstract":"We introduce Fisher consistency in the sense of unbiasedness as a desirable\nproperty for estimators of class prior probabilities. Lack of Fisher\nconsistency could be used as a criterion to dismiss estimators that are\nunlikely to deliver precise estimates in test datasets under prior probability\nand more general dataset shift. The usefulness of this unbiasedness concept is\ndemonstrated with three examples of classifiers used for quantification:\nAdjusted Classify & Count, EM-algorithm and CDE-Iterate. We find that Adjusted\nClassify & Count and EM-algorithm are Fisher consistent. A counter-example\nshows that CDE-Iterate is not Fisher consistent and, therefore, cannot be\ntrusted to deliver reliable estimates of class probabilities.","url_abs":"http://arxiv.org/abs/1701.05512v2","url_pdf":"http://arxiv.org/pdf/1701.05512v2.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":"fisher-consistency-for-prior-probability","repo_url":"https://github.com/albert-ziegler/unsupervised-calibration","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1701.05512","atlas_url":"https://app.syntology.ai/?focus=1701.05512","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.05512"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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