S-SIRUS: an explainability algorithm for spatial regression Random Forest

TL;DR

S-SIRUS is a novel spatial extension of the SIRUS algorithm that enhances explainability and predictive accuracy of Random Forests in spatially dependent data by producing concise rule lists.

Contribution

This paper introduces S-SIRUS, the first method to explain spatial regression Random Forests with stable, simple rules tailored for spatially correlated data.

Findings

S-SIRUS outperforms SIRUS in predictive accuracy with spatial data.

S-SIRUS generates shorter, more interpretable rule lists at higher spatial correlation levels.

The method improves understanding of RF predictions in spatial contexts.

Abstract

Random Forest (RF) is a widely used machine learning algorithm known for its flexibility, user-friendliness, and high predictive performance across various domains. However, it is non-interpretable. This can limit its usefulness in applied sciences, where understanding the relationships between predictors and response variable is crucial from a decision-making perspective. In the literature, several methods have been proposed to explain RF, but none of them addresses the challenge of explaining RF in the context of spatially dependent data. Therefore, this work aims to explain regression RF in the case of spatially dependent data by extracting a compact and simple list of rules. In this respect, we propose S-SIRUS, a spatial extension of SIRUS, the latter being a well-established regression rule algorithm able to extract a stable and short list of rules from the classical regression RF algorithm. A simulation study was conducted to evaluate the explainability capability of the proposed S-SIRUS, in comparison to SIRUS, by considering different levels of spatial dependence among the data. The results suggest that S-SIRUS exhibits a higher test predictive accuracy than SIRUS when spatial correlation is present. Moreover, for higher levels of spatial correlation, S-SIRUS produces a shorter list of rules, easing the explanation of the mechanism behind the predictions.