{"type":"ScholarlyArticle","title":"PyDL8.5: a Library for Learning Optimal Decision Trees","authors":[{"name":"Gaël Aglin","slug":"gael-aglin","given":"Gaël","family":"Aglin","affiliation":""},{"name":"Siegfried Nijssen","slug":"siegfried-nijssen","given":"Siegfried","family":"Nijssen","affiliation":""},{"name":"Pierre Schaus","slug":"pierre-schaus","given":"Pierre","family":"Schaus","affiliation":""}],"abstract":"Decision Trees (DTs) are widely used Machine Learning (ML) models with a broad range of applications. The interest in these models has increased even further in the context of Explainable AI (XAI), as decision trees of limited depth are very interpretable models. However, traditional algorithms for learning DTs are heuristic in nature; they may produce trees that are of suboptimal quality under depth constraints. We introduce PyDL8.5, a Python library to infer depth-constrained Optimal Decision Trees (ODTs). PyDL8.5 provides an interface for DL8.5, an efficient algorithm for inferring depth-constrained ODTs. The library provides an easy-to-use scikit-learn compatible interface. It cannot only be used for classification tasks, but also for  regression, clustering, and other tasks. We introduce an interface that allows users to easily implement these other learning tasks. We provide a number of examples of how to use this library.","doi":"10.24963/ijcai.2020/750","url":"https://ijcai.org/proceedings/2020/750","pdf":"https://ijcai.org/proceedings/2020/0750.pdf","markdown":"https://ijcai.org/proceedings/2020/750.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/750","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Demos","track":"Demos","number":750,"pages":{"first":5222,"last":5224},"keywords":["Machine Learning: general","Constraints and Satisfiability: general"],"videos":[],"references":[{"position":1,"raw":"[Aglin et al., 2020] Gaël Aglin, Siegfried Nijssen, and Pierre Schaus. 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