{"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/rule-induction-partitioning-estimator","title":"Rule Induction Partitioning Estimator","arxiv_id":"1807.04602","date":"2018-07-12","proceeding":null,"authors":["Vincent Margot","Jean-Patrick Baudry","Frederic Guilloux","Olivier Wintenberger"],"abstract":"RIPE is a novel deterministic and easily understandable prediction algorithm\ndeveloped for continuous and discrete ordered data. It infers a model, from a\nsample, to predict and to explain a real variable $Y$ given an input variable\n$X \\in \\mathcal X$ (features). The algorithm extracts a sparse set of\nhyperrectangles $\\mathbf r \\subset \\mathcal X$, which can be thought of as\nrules of the form If-Then. This set is then turned into a partition of the\nfeatures space $\\mathcal X$ of which each cell is explained as a list of rules\nwith satisfied their If conditions. The process of RIPE is illustrated on\nsimulated datasets and its efficiency compared with that of other usual\nalgorithms.","url_abs":"http://arxiv.org/abs/1807.04602v1","url_pdf":"http://arxiv.org/pdf/1807.04602v1.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":"rule-induction-partitioning-estimator","repo_url":"https://github.com/VMargot/RIPE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"rule-induction-partitioning-estimator","repo_url":"https://github.com/Advestis/RIPE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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}