# Regional Tree Regularization for Interpretability in Black Box Models

arXiv: https://arxiv.org/abs/1908.04494 · HTML: https://mlsanity.com/paper/1908.04494/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Leveraging Explanations in Interactive Machine Learning: An Overview](https://mlsanity.com/paper/2207.14526/) (2022)
- [Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning](https://mlsanity.com/paper/2212.03954/) (2022)
- [Computing Abductive Explanations for Boosted Trees](https://mlsanity.com/paper/2209.07740/) (2022)
- [The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms](https://mlsanity.com/paper/2203.06369/) (2022)
- [Learning Interpretable, High-Performing Policies for Autonomous Driving](https://mlsanity.com/paper/2202.02352/) (2022)
- [Tree-Based Leakage Inspection and Control in Concept Bottleneck Models](https://mlsanity.com/paper/2410.06352/) (2024)
- [A Survey of Neural Trees](https://mlsanity.com/paper/2209.03415/) (2022)
- [Defense Against Explanation Manipulation](https://mlsanity.com/paper/2111.04303/) (2021)
- [CA-Stream: Attention-based pooling for interpretable image recognition](https://mlsanity.com/paper/2404.14996/) (2024)
- [NN2Rules: Extracting Rule List from Neural Networks](https://mlsanity.com/paper/2207.12271/) (2022)
- [An Interpretable Power System Transient Stability Assessment Method with Expert Guiding Neural-Regression-Tree](https://mlsanity.com/paper/2404.02555/) (2024)
- [Enabling Regional Explainability by Automatic and Model-agnostic Rule Extraction](https://mlsanity.com/paper/2406.17885/) (2024)
- [Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence](https://mlsanity.com/paper/2607.08641/) (2026)

## Abstract (arXiv)

The lack of interpretability remains a barrier to the adoption of deep neural networks. Recently, tree regularization has been proposed to encourage deep neural networks to resemble compact, axis-aligned decision trees without significant compromises in accuracy. However, it may be unreasonable to expect that a single tree can predict well across all possible inputs. In this work, we propose regional tree regularization, which encourages a deep model to be well-approximated by several separate decision trees specific to predefined regions of the input space. Practitioners can define regions based on domain knowledge of contexts where different decision-making logic is needed. Across many datasets, our approach delivers more accurate predictions than simply training separate decision trees for each region, while producing simpler explanations than other neural net regularization schemes without sacrificing predictive power. Two healthcare case studies in critical care and HIV demonstrate how experts can improve understanding of deep models via our approach.