Fast Sparse Decision Tree Optimization via Reference Ensembles


  • Hayden McTavish University of California, San Diego University of British Columbia
  • Chudi Zhong Duke University
  • Reto Achermann University of British Columbia
  • Ilias Karimalis University of British Columbia
  • Jacques Chen University of British Columbia
  • Cynthia Rudin Duke University
  • Margo Seltzer University of British Columbia



Philosophy And Ethics Of AI (PEAI), Humans And AI (HAI), Search And Optimization (SO), Machine Learning (ML)


Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady effort since the 1960's, breakthroughs have been made on the problem only within the past few years, primarily on the problem of finding optimal sparse decision trees. However, current state-of-the-art algorithms often require impractical amounts of computation time and memory to find optimal or near-optimal trees for some real-world datasets, particularly those having several continuous-valued features. Given that the search spaces of these decision tree optimization problems are massive, can we practically hope to find a sparse decision tree that competes in accuracy with a black box machine learning model? We address this problem via smart guessing strategies that can be applied to any optimal branch-and-bound-based decision tree algorithm. The guesses come from knowledge gleaned from black box models. We show that by using these guesses, we can reduce the run time by multiple orders of magnitude while providing bounds on how far the resulting trees can deviate from the black box's accuracy and expressive power. Our approach enables guesses about how to bin continuous features, the size of the tree, and lower bounds on the error for the optimal decision tree. Our experiments show that in many cases we can rapidly construct sparse decision trees that match the accuracy of black box models. To summarize: when you are having trouble optimizing, just guess.




How to Cite

McTavish, H., Zhong, C., Achermann, R., Karimalis, I., Chen, J., Rudin, C., & Seltzer, M. (2022). Fast Sparse Decision Tree Optimization via Reference Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 36(9), 9604-9613.



AAAI Technical Track on Philosophy and Ethics of AI