PyDL8.5: a Library for Learning Optimal Decision Trees

By Gaël Aglin, Siegfried Nijssen, Pierre Schaus. 2020

33 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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Citations in the corpus

MurTree: Optimal Decision Trees via Dynamic Programming and Search (2020, methodology): “5 (Aglin et al. (2020a)).”
Optimal Decision Trees for Nonlinear Metrics (2020, methodology): “Aglin, Nijssen, and Schaus (2020) developed DL8.”
On Explaining Decision Trees (2020, background): “5 [1,2] complete benchmark set, and are included in the supplementary materials.)”