PyDL8.5: a Library for Learning Optimal Decision Trees
(2020) International Joint Conference on Artificial Intelligence
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- Abstract
- Decision Trees (DTs) are widely used Machine Learning (ML) models with a broad range of appli- cations. The interest in these models has increased even further in the context of Explainable AI (XAI), as decision trees of limited depth are very inter- pretable 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 re- gression, 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.
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Citations
Aglin, G., Nijssen, S., & Schaus, P. (2020). PyDL8.5: a Library for Learning Optimal Decision Trees. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, p. 5222-5224. https://hdl.handle.net/2078.5/269948