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

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.

A path in regression Random Forest looking for spatial dependence: a taxonomy and a systematic review

Random Forest (RF) is a well-known data-driven algorithm applied in several fields thanks to its flexibility in modeling the relationship between the response variable and the predictors, also in case of strong non-linearities. In environmental applications, it often occurs that the phenomenon of interest may present spatial and/or temporal dependence that is not taken explicitly into account by RF in its standard version. In this work, we propose a taxonomy to classify strategies according to when (Pre-, In- and/or Post-processing) they try to include the spatial information into regression RF. Moreover, we provide a systematic review and classify the most recent strategies adopted to "adjust" regression RF to spatially dependent data, based on the criteria provided by the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA). The latter consists of a reproducible methodology for collecting and processing existing literature on a specified topic from different sources. PRISMA starts with a query and ends with a set of scientific documents to review: we performed an online query on the 25$^{th}$ October 2022 and, in the end, 32 documents were considered for review. The employed methodological strategies and the application fields considered in the 32 scientific documents are described and discussed.