Confidence intervals for the random forest generalization error

[2112.06101] Confidence intervals for the random forest generalization error Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Machine Learning arXiv:2112.06101 (stat) [Submitted on 11 Dec 2021 ( v1 ), last revised 11 Mar 2022 (this version, v3)] Title: Confidence intervals for the random forest generalization error Authors: Paulo C. Marques F View a PDF of the paper titled Confidence intervals for the random forest generalization error, by Paulo C. Marques F View PDF HTML (experimental) Abstract: We show that the byproducts of the standard training process of a random forest yield not only the well known and almost computationally free out-of-bag point estimate of the model generalization error, but also give a direct path to compute confidence intervals for the generalization error which avoids processes of data splitting and model retraining. Besides the low computational cost involved in their construction, these confidence intervals are shown through simulations to have good coverage and appropriate shrinking rate of their width in terms of the training sample size. Comments: 10 pages Subjects: Machine Learning (stat.ML) ; Machine Learning (cs.LG) Cite as: arXiv:2112.06101 [stat.ML] (or arXiv:2112.06101v3 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2112.06101 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Pattern Recognition Letters 158, 171-175 (2022) Related DOI : https://doi.org/10.1016/j.patrec.2022.04.031 Focus to learn more DOI(s) linking to related resources Submission history From: Paulo C. Marques F. [ view email ] [v1] Sat, 11 Dec 2021 23:58:14 UTC (8 KB) [v2] Tue, 8 Mar 2022 16:49:43 UTC (9 KB) [v3] Fri, 11 Mar 2022 15:30:20 UTC (9 KB) Full-text links: Access Paper: View a PDF of the paper titled Confidence intervals for the random forest generalization error, by Paulo C. Marques F View PDF HTML (experimental) TeX Source view license Current browse context: stat.ML < prev | next > new | recent | 2021-12 Change to browse by: cs cs.LG stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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