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Poster
Optimal Sparse Decision Trees
Xiyang Hu · Cynthia Rudin · Margo Seltzer

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #15

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980's. The problem that has plagued decision tree algorithms since their inception is their lack of optimality, or lack of guarantees of closeness to optimality: decision tree algorithms are often greedy or myopic, and sometimes produce unquestionably suboptimal models. Hardness of decision tree optimization is both a theoretical and practical obstacle, and even careful mathematical programming approaches have not been able to solve these problems efficiently. This work introduces the first practical algorithm for optimal decision trees for binary variables. The algorithm is a co-design of analytical bounds that reduce the search space and modern systems techniques, including data structures and a custom bit-vector library. Our experiments highlight advantages in scalability, speed, and proof of optimality.

Author Information

Xiyang Hu (Carnegie Mellon University)
Cynthia Rudin (Duke)
Margo Seltzer (University of British Columbia)

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