3 papers
stat.ME2025
INLA-RF: A Hybrid Modeling Strategy for Spatio-Temporal Environmental Data
Mario Figueira, Michela Cameletti, Luca Patelli
Environmental processes often exhibit complex, non-linear patterns and discontinuities across space and time, posing significant challenges for traditional geostatistical modeling…
stat.ML2024
S-SIRUS: an explainability algorithm for spatial regression Random Forest
Luca Patelli, Natalia Golini, Rosaria Ignaccolo +1
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…
stat.ML2023
A path in regression Random Forest looking for spatial dependence: a taxonomy and a systematic review
Luca Patelli, Michela Cameletti, Natalia Golini +1
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 pred…