# SIRUS: Stable and Interpretable RUle Set for Classification

## TL;DR

SIRUS is a new classification method that produces simple, stable, and interpretable rule sets with accuracy comparable to random forests, suitable for critical decision-making contexts.

## Contribution

The paper introduces SIRUS, a stable and interpretable rule-based classifier derived from random forests, addressing the interpretability and stability issues of existing models.

## Key findings

- SIRUS achieves higher stability than existing methods.
- SIRUS maintains predictive accuracy close to random forests.
- The approach is validated through extensive experiments.

## Abstract

State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as "black-boxes" because of the high number and complexity of operations involved in their prediction mechanism. This lack of interpretability is a strong limitation for applications involving critical decisions, typically the analysis of production processes in the manufacturing industry. In such critical contexts, models have to be interpretable, i.e., simple, stable, and predictive. To address this issue, we design SIRUS (Stable and Interpretable RUle Set), a new classification algorithm based on random forests, which takes the form of a short list of rules. While simple models are usually unstable with respect to data perturbation, SIRUS achieves a remarkable stability improvement over cutting-edge methods. Furthermore, SIRUS inherits a predictive accuracy close to random forests, combined with the simplicity of decision trees. These properties are assessed both from a theoretical and empirical point of view, through extensive numerical experiments based on our R/C++ software implementation sirus available from CRAN.