# Looking Deeper into Tabular LIME

arXiv: https://arxiv.org/abs/2008.11092 · HTML: https://mlsanity.com/paper/2008.11092/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 20 of 20)

- [What does LIME really see in images?](https://mlsanity.com/paper/2102.06307/) (2021)
- [GLIME: General, Stable and Local LIME Explanation](https://mlsanity.com/paper/2311.15722/) (2023)
- [Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack](https://mlsanity.com/paper/2305.12351/) (2023)
- [Ensembles of Random SHAPs](https://mlsanity.com/paper/2103.03302/) (2021)
- [A k-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning](https://mlsanity.com/paper/2211.02166/) (2022)
- [Which LIME should I trust? Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2503.24365/) (2025)
- [An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data](https://mlsanity.com/paper/2106.09111/) (2021)
- [Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions](https://mlsanity.com/paper/2206.12481/) (2022)
- [Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP](https://mlsanity.com/paper/2209.07623/) (2022)
- [SMACE: A New Method for the Interpretability of Composite Decision Systems](https://mlsanity.com/paper/2111.08749/) (2021)
- [Interpretable Ensembles of Hyper-Rectangles as Base Models](https://mlsanity.com/paper/2303.08625/) (2023)
- [ExpProof : Operationalizing Explanations for Confidential Models with ZKPs](https://mlsanity.com/paper/2502.03773/) (2025)
- [Attribution Explanations for Deep Neural Networks: A Theoretical Perspective](https://mlsanity.com/paper/2508.07636/) (2025)
- [Towards Robust and Accurate Stability Estimation of Local Surrogate Models in Text-based Explainable AI](https://mlsanity.com/paper/2501.02042/) (2025)
- [Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths](https://mlsanity.com/paper/2203.02295/) (2022)
- [SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models](https://mlsanity.com/paper/2312.06638/) (2023)
- [Dual feature-based and example-based explanation methods](https://mlsanity.com/paper/2401.16294/) (2024)
- [ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings](https://mlsanity.com/paper/2508.13672/) (2025)
- [α-TCAV: A Unified Framework for Testing with Concept Activation Vectors](https://mlsanity.com/paper/2605.15688/) (2026)
- [When Superpixels Fail on Documents: A Study of Segmentation for LIME Explanations](https://mlsanity.com/paper/2609.07462/) (2026)

## Abstract (arXiv)

In this paper, we present a thorough theoretical analysis of the default implementation of LIME in the case of tabular data. We prove that in the large sample limit, the interpretable coefficients provided by Tabular LIME can be computed in an explicit way as a function of the algorithm parameters and some expectation computations related to the black-box model. When the function to explain has some nice algebraic structure (linear, multiplicative, or sparsely depending on a subset of the coordinates), our analysis provides interesting insights into the explanations provided by LIME. These can be applied to a range of machine learning models including Gaussian kernels or CART random forests. As an example, for linear functions we show that LIME has the desirable property to provide explanations that are proportional to the coefficients of the function to explain and to ignore coordinates that are not used by the function to explain. For partition-based regressors, on the other side, we show that LIME produces undesired artifacts that may provide misleading explanations.