Learning Certifiably Optimal Rule Lists for Categorical Data

author: Elaine Angelino, Department of Electrical Engineering and Computer Sciences, UC Berkeley
published: Oct. 9, 2017,   recorded: August 2017,   views: 2
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Description

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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