Journal
KDD'17: PROCEEDINGS OF THE 23RD ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING
Volume -, Issue -, Pages 35-44Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3097983.3098047
Keywords
Rule lists; Decision trees; Optimization; Interpretable models
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Funding
- Miller Institute for Basic Research in Science, University of California, Berkeley
- MIT-Lincoln Labs
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We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm provides the optimal solution, with a certificate of optimality. By leveraging algorithmic bounds, efficient data structures, and computational reuse, we achieve several orders of magnitude speedup in time and a massive reduction of memory consumption. We demonstrate that our approach produces optimal rule lists on practical problems in seconds. This framework is a novel alternative to CART and other decision tree methods.
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