# MAIRE -- A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers

arXiv: https://arxiv.org/abs/2011.01506 · HTML: https://mlsanity.com/paper/2011.01506/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs](https://mlsanity.com/paper/2108.13828/) (2021)
- [Interpretable Regional Descriptors: Hyperbox-Based Local Explanations](https://mlsanity.com/paper/2305.02780/) (2023)
- [Explainable Supervised Domain Adaptation](https://mlsanity.com/paper/2205.09943/) (2022)
- [MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model](https://mlsanity.com/paper/2503.22731/) (2025)
- [Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor](https://mlsanity.com/paper/2208.00335/) (2022)
- [Guarantee Regions for Local Explanations](https://mlsanity.com/paper/2402.12737/) (2024)

## Abstract (arXiv)

The paper introduces a novel framework for extracting model-agnostic human interpretable rules to explain a classifier's output. The human interpretable rule is defined as an axis-aligned hyper-cuboid containing the instance for which the classification decision has to be explained. The proposed procedure finds the largest (high textitcoverage) axis-aligned hyper-cuboid such that a high percentage of the instances in the hyper-cuboid have the same class label as the instance being explained (high textitprecision). Novel approximations to the coverage and precision measures in terms of the parameters of the hyper-cuboid are defined. They are maximized using gradient-based optimizers. The quality of the approximations is rigorously analyzed theoretically and experimentally. Heuristics for simplifying the generated explanations for achieving better interpretability and a greedy selection algorithm that combines the local explanations for creating global explanations for the model covering a large part of the instance space are also proposed. The framework is model agnostic, can be applied to any arbitrary classifier, and all types of attributes (including continuous, ordered, and unordered discrete). The wide-scale applicability of the framework is validated on a variety of synthetic and real-world datasets from different domains (tabular, text, and image).