Scalable Bayesian rule lists

Published

Conference Paper

© Copyright 2017 by the authors(s). We present an algorithm for building probabilistic rule lists that is two orders of magnitude faster than previous work. Rule list algorithms are competitors for decision tree algorithms. They are associative classifiers, in that they are built from pre-mined association rules. They have a logical structure that is a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree. Instead of using greedy splitting and pruning like decision tree algorithms, we aim to fully optimize over rule lists, striking a practical balance between accuracy, inter-pretability, and computational speed. The algorithm presented here uses a mixture of theoretical bounds (tight enough to have practical implications as a screening or bounding procedure), computational reuse, and highly tuned language libraries to achieve computational efficiency. Currently, for many practical problems, this method achieves better accuracy and sparsity than decision trees, with practical running times. The predictions in each leaf are probabilistic.

Duke Authors

Cited Authors

  • Yang, H; Rudin, C; Seltzer, M

Published Date

  • January 1, 2017

Published In

  • 34th International Conference on Machine Learning, Icml 2017

Volume / Issue

  • 8 /

Start / End Page

  • 5971 - 5980

International Standard Book Number 13 (ISBN-13)

  • 9781510855144

Citation Source

  • Scopus