# Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems

arXiv: https://arxiv.org/abs/1504.07614 · HTML: https://mlsanity.com/paper/1504.07614/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2015.

## Built on by (most cited first, 2 of 2)

- [Learning Optimal Predictive Checklists](https://mlsanity.com/paper/2112.01020/) (2021)
- [Trade-off Between Efficiency and Consistency for Removal-based Explanations](https://mlsanity.com/paper/2210.17426/) (2022)

## Abstract (arXiv)

We present a machine learning algorithm for building classifiers that are comprised of a small number of disjunctions of conjunctions (or's of and's). An example of a classifier of this form is as follows: If X satisfies (x1 = 'blue' AND x3 = 'middle') OR (x1 = 'blue' AND x2 = '<15') OR (x1 = 'yellow'), then we predict that Y=1, ELSE predict Y=0. An attribute-value pair is called a literal and a conjunction of literals is called a pattern. Models of this form have the advantage of being interpretable to human experts, since they produce a set of conditions that concisely describe a specific class. We present two probabilistic models for forming a pattern set, one with a Beta-Binomial prior, and the other with Poisson priors. In both cases, there are prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domain-specific definition of interpretability. We provide two scalable MAP inference approaches: a pattern level search, which involves association rule mining, and a literal level search. We show stronger priors reduce computation. We apply the Bayesian Or's of And's (BOA) model to predict user behavior with respect to in-vehicle context-aware personalized recommender systems.