# LoRMIkA: Local rule-based model interpretability with k-optimal associations

arXiv: https://arxiv.org/abs/1908.03840 · HTML: https://mlsanity.com/paper/1908.03840/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2019.

## Built on by (most cited first, 10 of 10)

- [On Locality of Local Explanation Models](https://mlsanity.com/paper/2106.14648/) (2021)
- [LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data](https://mlsanity.com/paper/2202.07766/) (2022)
- [Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets](https://mlsanity.com/paper/2201.07724/) (2022)
- [Efficient Learning of Interpretable Classification Rules](https://mlsanity.com/paper/2205.06936/) (2022)
- [SQAPlanner: Generating Data-Informed Software Quality Improvement Plans](https://mlsanity.com/paper/2102.09687/) (2021)
- [LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts](https://mlsanity.com/paper/2111.07001/) (2021)
- [Beyond Average Performance -- exploring regions of deviating performance for black box classification models](https://mlsanity.com/paper/2109.08216/) (2021)
- [The Shape of Explanations: A Topological Account of Rule-Based Explanations in Machine Learning](https://mlsanity.com/paper/2301.09042/) (2023)
- [Introducing User Feedback-based Counterfactual Explanations (UFCE)](https://mlsanity.com/paper/2403.00011/) (2024)
- [The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond](https://mlsanity.com/paper/2311.04007/) (2023)

## Abstract (arXiv)

As we rely more and more on machine learning models for real-life decision-making, being able to understand and trust the predictions becomes ever more important. Local explainer models have recently been introduced to explain the predictions of complex machine learning models at the instance level. In this paper, we propose Local Rule-based Model Interpretability with k-optimal Associations (LoRMIkA), a novel model-agnostic approach that obtains k-optimal association rules from a neighbourhood of the instance to be explained. Compared with other rule-based approaches in the literature, we argue that the most predictive rules are not necessarily the rules that provide the best explanations. Consequently, the LoRMIkA framework provides a flexible way to obtain predictive and interesting rules. It uses an efficient search algorithm guaranteed to find the k-optimal rules with respect to objectives such as confidence, lift, leverage, coverage, and support. It also provides multiple rules which explain the decision and counterfactual rules, which give indications for potential changes to obtain different outputs for given instances. We compare our approach to other state-of-the-art approaches in local model interpretability on three different datasets and achieve competitive results in terms of local accuracy and interpretability.