Interpretable Decision Trees Through MaxSAT
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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.
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
4 of 31 references.
References · 4
- 2017PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparisoncited by 140
- 2019Optimal Sparse Decision Treescited by 105
- 2020PMLB v1.0: An open source dataset collection for benchmarking machine learning methodscited by 58
- 2021Connecting Interpretability and Robustness in Decision Trees through Separationcited by 16
Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.