{"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/finding-influential-training-samples-for","title":"Finding Influential Training Samples for Gradient Boosted Decision Trees","arxiv_id":"1802.06640","date":"2018-02-19","proceeding":"ICML 2018 7","authors":["Boris Sharchilev","Yury Ustinovsky","Pavel Serdyukov","Maarten de Rijke"],"abstract":"We address the problem of finding influential training samples for a\nparticular case of tree ensemble-based models, e.g., Random Forest (RF) or\nGradient Boosted Decision Trees (GBDT). A natural way of formalizing this\nproblem is studying how the model's predictions change upon leave-one-out\nretraining, leaving out each individual training sample. Recent work has shown\nthat, for parametric models, this analysis can be conducted in a\ncomputationally efficient way. We propose several ways of extending this\nframework to non-parametric GBDT ensembles under the assumption that tree\nstructures remain fixed. Furthermore, we introduce a general scheme of\nobtaining further approximations to our method that balance the trade-off\nbetween performance and computational complexity. We evaluate our approaches on\nvarious experimental setups and use-case scenarios and demonstrate both the\nquality of our approach to finding influential training samples in comparison\nto the baselines and its computational efficiency.","url_abs":"http://arxiv.org/abs/1802.06640v2","url_pdf":"http://arxiv.org/pdf/1802.06640v2.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":"finding-influential-training-samples-for","repo_url":"https://github.com/bsharchilev/influence_boosting","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.06640","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}