ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription

[2307.15691] ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Machine Learning arXiv:2307.15691 (stat) [Submitted on 28 Jul 2023 ( v1 ), last revised 15 Aug 2026 (this version, v4)] Title: ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription Authors: Patrick Vossler , Nathan Justin , Sina Aghaei , Nathanael Jo , Andrés Gómez , Phebe Vayanos View a PDF of the paper titled ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription, by Patrick Vossler and 5 other authors View PDF HTML (experimental) Abstract: ODTlearn is an open source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the state-of-the-art mixed-integer optimization (MIO) framework proposed in Aghaei et al. (2025). The current version of the package provides implementations for learning optimal classification trees, optimal fair classification trees, optimal prescriptive trees from observational data, and optimal classification trees robust to distribution shifts. We have designed the package to be easy to maintain and extend as new optimal decision tree problem classes, reformulation strategies, and solution algorithms are introduced. To this end, the package follows object-oriented design principles and supports both commercial (Gurobi) and open source (COIN-OR branch and cut) solvers. The package documentation, user guide, installation instructions, link to source code, and instructions for submitting bug reports and feature requests can all be found at this https URL . Comments: 9 pages, 2 figures Subjects: Machine Learning (stat.ML) ; Machine Learning (cs.LG); Optimization and Control (math.OC) Cite as: arXiv:2307.15691 [stat.ML] (or arXiv:2307.15691v4 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2307.15691 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Nathan Justin [ view email ] [v1] Fri, 28 Jul 2023 17:37:47 UTC (249 KB) [v2] Mon, 13 Nov 2023 01:56:51 UTC (233 KB) [v3] Sat, 30 Aug 2025 18:47:23 UTC (42 KB) [v4] Sat, 15 Aug 2026 00:31:43 UTC (50 KB) Full-text links: Access Paper: View a PDF of the paper titled ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription, by Patrick Vossler and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ML < prev | next > new | recent | 2023-07 Change to browse by: cs cs.LG math math.OC 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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