# Interpretable Decision Trees Through MaxSAT

arXiv: https://arxiv.org/abs/2110.13854 · HTML: https://mlsanity.com/paper/2110.13854/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 26 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison](https://mlsanity.com/paper/1703.00512/) (2017)
- [Optimal Sparse Decision Trees](https://mlsanity.com/paper/1904.12847/) (2019)
- [PMLB v1.0: An open source dataset collection for benchmarking machine learning methods](https://mlsanity.com/paper/2012.00058/) (2020)
- [Connecting Interpretability and Robustness in Decision Trees through Separation](https://mlsanity.com/paper/2102.07048/) (2021)

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

- [Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance](https://mlsanity.com/paper/2409.12788/) (2024)
- [A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees](https://mlsanity.com/paper/2609.38194/) (2026)

## Abstract (arXiv)

We present an approach to improve the accuracy-interpretability trade-off of Machine Learning (ML) Decision Trees (DTs). In particular, we apply Maximum Satisfiability technology to compute Minimum Pure DTs (MPDTs). We improve the runtime of previous approaches and, show that these MPDTs can outperform the accuracy of DTs generated with the ML framework sklearn.