Explainable models via compression of tree ensembles

• Published in: Machine learning Vol. 113; no. 3; pp. 1303 - 1328
• Main Authors: Yan, Siwen, Natarajan, Sriraam, Joshi, Saket, Khardon, Roni, Tadepalli, Prasad
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Compressing; Computer Science; Control; Decision trees; Logic; Machine Learning; Mechatronics; Natural Language Processing (NLP); Neural networks; Robotics; Semantics; Simulation and Modeling; Special Issue on Learning and Reasoning 2022; Variables; TILDE; Tree combination; First order logic; Ensemble

Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be some of the most effective learning methods in the area of probabilistic logic models (PLMs). While effective, they lose one of the most important benefits of PLMs—interpretability. In this paper we consid...