Falling Rule Lists

Abstract

Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are inspired by healthcare applications where patients would be stratified into risk sets and the highest at-risk patients should be considered first. We provide a Bayesian framework for learning falling rule lists that does not rely on traditional greedy decision tree learning methods.

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Cite

Text

Wang and Rudin. "Falling Rule Lists." International Conference on Artificial Intelligence and Statistics, 2015.

Markdown

[Wang and Rudin. "Falling Rule Lists." International Conference on Artificial Intelligence and Statistics, 2015.](https://mlanthology.org/aistats/2015/wang2015aistats-falling/)

BibTeX

@inproceedings{wang2015aistats-falling,
  title     = {{Falling Rule Lists}},
  author    = {Wang, Fulton and Rudin, Cynthia},
  booktitle = {International Conference on Artificial Intelligence and Statistics},
  year      = {2015},
  url       = {https://mlanthology.org/aistats/2015/wang2015aistats-falling/}
}