# Falling Rule Lists

arXiv: https://arxiv.org/abs/1411.5899 · HTML: https://mlsanity.com/paper/1411.5899/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2014.

## Built on by (most cited first, 30 of 59)

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- [Computing Abductive Explanations for Boosted Trees](https://mlsanity.com/paper/2209.07740/) (2022)
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- [Computing the Collection of Good Models for Rule Lists](https://mlsanity.com/paper/2204.11285/) (2022)
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- [Explainable Bayesian Optimization](https://mlsanity.com/paper/2401.13334/) (2024)
- [Efficient learning of large sets of locally optimal classification rules](https://mlsanity.com/paper/2301.09936/) (2023)
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- [Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective](https://mlsanity.com/paper/2303.13299/) (2023)
- [Understanding Disparities in Post Hoc Machine Learning Explanation](https://mlsanity.com/paper/2401.14539/) (2024)
- [An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets](https://mlsanity.com/paper/2109.09160/) (2021)
- [Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets](https://mlsanity.com/paper/2201.07724/) (2022)
- [Efficient Learning of Interpretable Classification Rules](https://mlsanity.com/paper/2205.06936/) (2022)
- [Fair Decision Rules for Binary Classification](https://mlsanity.com/paper/2107.01325/) (2021)

## Abstract (arXiv)

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.