Learning certifiably optimal rule lists for categorical data
Learning certifiably optimal rule lists for categorical data
Elaine Angelino (UC Berkeley)
Nicholas Larus-Stone (Harvard)
Daniel Alabi (Harvard)
Margo Seltzer (Harvard University)
Cynthia Rudin (Duke)
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.
More on http://www.kdd.org/kdd2017/
Видео Learning certifiably optimal rule lists for categorical data канала KDD2017 video
Elaine Angelino (UC Berkeley)
Nicholas Larus-Stone (Harvard)
Daniel Alabi (Harvard)
Margo Seltzer (Harvard University)
Cynthia Rudin (Duke)
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.
More on http://www.kdd.org/kdd2017/
Видео Learning certifiably optimal rule lists for categorical data канала KDD2017 video
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