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

arXiv: https://arxiv.org/abs/2408.05537 · HTML: https://mlsanity.com/paper/2408.05537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 10 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets](https://mlsanity.com/paper/1406.7343/) (2014)
- [Veridical Data Science](https://mlsanity.com/paper/1901.08152/) (2019)
- [Interpreting Tree Ensembles with inTrees](https://mlsanity.com/paper/1408.5456/) (2014)
- [Interpretable Random Forests via Rule Extraction](https://mlsanity.com/paper/2004.14841/) (2020)
- [Cross-Validation for Correlated Data](https://mlsanity.com/paper/1904.02438/) (2019)
- [A path in regression Random Forest looking for spatial dependence: a taxonomy and a systematic review](https://mlsanity.com/paper/2303.04693/) (2023)

## Abstract (arXiv)

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.