# TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning

arXiv: https://arxiv.org/abs/1611.07429 · HTML: https://mlsanity.com/paper/1611.07429/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2016.

## Built on by (most cited first, 9 of 9)

- [Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2103.04244/) (2021)
- [Neural Basis Models for Interpretability](https://mlsanity.com/paper/2205.14120/) (2022)
- [A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future](https://mlsanity.com/paper/2412.14056/) (2024)
- [Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets](https://mlsanity.com/paper/2201.07724/) (2022)
- [Unsupervised discovery of Interpretable Visual Concepts](https://mlsanity.com/paper/2309.00018/) (2023)
- [Debunking Grad-ECLIP: A Comprehensive Study on Its Incorrectness and Fundamental Principles for Model Interpretation](https://mlsanity.com/paper/2605.12952/) (2026)
- [Rule-Extraction Methods From Feedforward Neural Networks: A Systematic Literature Review](https://mlsanity.com/paper/2312.12878/) (2023)
- [Transforming gradient-based techniques into interpretable methods](https://mlsanity.com/paper/2401.14434/) (2024)
- [Explainable AI the Latest Advancements and New Trends](https://mlsanity.com/paper/2505.07005/) (2025)

## Abstract (arXiv)

With the advent of highly predictive but opaque deep learning models, it has become more important than ever to understand and explain the predictions of such models. Existing approaches define interpretability as the inverse of complexity and achieve interpretability at the cost of accuracy. This introduces a risk of producing interpretable but misleading explanations. As humans, we are prone to engage in this kind of behavior citemythos. In this paper, we take a step in the direction of tackling the problem of interpretability without compromising the model accuracy. We propose to build a Treeview representation of the complex model via hierarchical partitioning of the feature space, which reveals the iterative rejection of unlikely class labels until the correct association is predicted.