{"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/learning-optimal-decision-trees-from-large","title":"Learning Optimal Decision Trees from Large Datasets","arxiv_id":"1904.06314","date":"2019-04-12","proceeding":null,"authors":["Florent Avellaneda"],"abstract":"Inferring a decision tree from a given dataset is one of the classic problems\nin machine learning. This problem consists of buildings, from a labelled\ndataset, a tree such that each node corresponds to a class and a path between\nthe tree root and a leaf corresponds to a conjunction of features to be\nsatisfied in this class. Following the principle of parsimony, we want to infer\na minimal tree consistent with the dataset. Unfortunately, inferring an optimal\ndecision tree is known to be NP-complete for several definitions of optimality.\nHence, the majority of existing approaches relies on heuristics, and as for the\nfew exact inference approaches, they do not work on large data sets. In this\npaper, we propose a novel approach for inferring a decision tree of a minimum\ndepth based on the incremental generation of Boolean formula. The experimental\nresults indicate that it scales sufficiently well and the time it takes to run\ngrows slowly with the size of dataset.","url_abs":"http://arxiv.org/abs/1904.06314v1","url_pdf":"http://arxiv.org/pdf/1904.06314v1.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":"learning-optimal-decision-trees-from-large","repo_url":"https://github.com/FlorentAvellaneda/InferDT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}