# Feature Importance Measurement based on Decision Tree Sampling

arXiv: https://arxiv.org/abs/2307.13333 · HTML: https://mlsanity.com/paper/2307.13333/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Learning Certifiably Optimal Rule Lists for Categorical Data](https://mlsanity.com/paper/1704.01701/) (2017)
- [In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction](https://mlsanity.com/paper/2005.04176/) (2020)
- [FasterRisk: Fast and Accurate Interpretable Risk Scores](https://mlsanity.com/paper/2210.05846/) (2022)
- [Fast and More Powerful Selective Inference for Sparse High-order Interaction Model](https://mlsanity.com/paper/2106.04929/) (2021)
- [A Confidence Machine for Sparse High-Order Interaction Model](https://mlsanity.com/paper/2205.14317/) (2022)

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

- [An Information-Theoretic Approach to Analyze NLP Classification Tasks](https://mlsanity.com/paper/2402.00978/) (2024)
- [TreeNet: Layered Decision Ensembles](https://mlsanity.com/paper/2510.09654/) (2025)

## Abstract (arXiv)

Random forest is effective for prediction tasks but the randomness of tree generation hinders interpretability in feature importance analysis. To address this, we proposed DT-Sampler, a SAT-based method for measuring feature importance in tree-based model. Our method has fewer parameters than random forest and provides higher interpretability and stability for the analysis in real-world problems. An implementation of DT-Sampler is available at https://github.com/tsudalab/DT-sampler.