{"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/how-interpretable-and-trustworthy-are-gams","title":"How Interpretable and Trustworthy are GAMs?","arxiv_id":"2006.06466","date":"2020-06-11","proceeding":null,"authors":["Chun-Hao Chang","Sarah Tan","Ben Lengerich","Anna Goldenberg","Rich Caruana"],"abstract":"Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning. However, there are many algorithms for training GAMs, and these can learn different or even contradictory models, while being equally accurate. Which GAM should we trust? 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Our results suggest that inductive bias plays a crucial role in what interpretable models learn and that tree-based GAMs represent the best balance of sparsity, fidelity and accuracy and thus appear to be the most trustworthy GAM.","url_abs":"https://arxiv.org/abs/2006.06466v2","url_pdf":"https://arxiv.org/pdf/2006.06466v2.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":"how-interpretable-and-trustworthy-are-gams","repo_url":"https://github.com/zzzace2000/GAMs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"how-interpretable-and-trustworthy-are-gams","repo_url":"https://github.com/zzzace2000/nodegam","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"additive-models","task_name":"Additive models"},{"task_slug":"inductive-bias","task_name":"Inductive Bias"},{"task_slug":"interpretable-machine-learning","task_name":"Interpretable Machine Learning"}],"methods":[{"method_slug":"gam","method_name":"GAM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.06466","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06466"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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