# LIMEtree: Consistent and Faithful Surrogate Explanations of Multiple Classes

arXiv: https://arxiv.org/abs/2005.01427 · HTML: https://mlsanity.com/paper/2005.01427/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 8 of 8)

- [What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks](https://mlsanity.com/paper/2403.12730/) (2024)
- [Navigating Explanatory Multiverse Through Counterfactual Path Geometry](https://mlsanity.com/paper/2306.02786/) (2023)
- [Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations](https://mlsanity.com/paper/2303.00934/) (2023)
- [Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations](https://mlsanity.com/paper/2502.17007/) (2025)
- [LIPEx-Locally Interpretable Probabilistic Explanations-To Look Beyond The True Class](https://mlsanity.com/paper/2310.04856/) (2023)
- [ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings](https://mlsanity.com/paper/2508.13672/) (2025)
- [Beyond Linear Surrogates: High-Fidelity Local Explanations for Black-Box Models](https://mlsanity.com/paper/2512.05556/) (2025)
- [Enhancing LIME using Neural Decision Trees](https://mlsanity.com/paper/2603.20919/) (2026)

## Abstract (arXiv)

Explainable artificial intelligence provides tools to better understand predictive models and their decisions, but many such methods are limited to producing insights with respect to a single class. When generating explanations for several classes, reasoning over them to obtain a comprehensive view may be difficult since they can present competing or contradictory evidence. To address this challenge we introduce the novel paradigm of multi-class explanations. We outline the theory behind such techniques and propose a local surrogate model based on multi-output regression trees -- called LIMEtree -- that offers faithful and consistent explanations of multiple classes for individual predictions while being post-hoc, model-agnostic and data-universal. On top of strong fidelity guarantees, our implementation delivers a range of diverse explanation types, including counterfactual statements favoured in the literature. We evaluate our algorithm with respect to explainability desiderata, through quantitative experiments and via a pilot user study, on image and tabular data classification tasks, comparing it to LIME, which is a state-of-the-art surrogate explainer. Our contributions demonstrate the benefits of multi-class explanations and wide-ranging advantages of our method across a diverse set of scenarios.