# Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance

arXiv: https://arxiv.org/abs/1611.05817 · HTML: https://mlsanity.com/paper/1611.05817/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2016.

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

- [To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods](https://mlsanity.com/paper/2106.00461/) (2021)
- [Self-explaining deep models with logic rule reasoning](https://mlsanity.com/paper/2210.07024/) (2022)
- [Recent Advances in Malware Detection: Graph Learning and Explainability](https://mlsanity.com/paper/2502.10556/) (2025)
- [On the Effectiveness of Interpretable Feedforward Neural Network](https://mlsanity.com/paper/2111.02303/) (2021)
- [Ensemble Interpretation: A Unified Method for Interpretable Machine Learning](https://mlsanity.com/paper/2312.06255/) (2023)
- [T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients](https://mlsanity.com/paper/2404.16495/) (2024)
- [DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray images](https://mlsanity.com/paper/2105.09683/) (2021)
- [Comparison of decision trees with Local Interpretable Model-Agnostic Explanations (LIME) technique and multi-linear regression for explaining support vector regression model in terms of root mean square error (RMSE) values](https://mlsanity.com/paper/2404.07046/) (2024)
- [neuralGAM: An R Package for Fitting Generalized Additive Neural Networks](https://mlsanity.com/paper/2505.08610/) (2025)

## Abstract (arXiv)

At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model's behavior, precision to how accurate humans are in those predictions, and effort is either the up-front effort required in interpreting the model, or the effort required to make predictions about a model's behavior. In this work, we propose anchor-LIME (aLIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear. We compare aLIME to linear LIME with simulated experiments, and demonstrate the flexibility of aLIME with qualitative examples from a variety of domains and tasks.