Machine learning–assisted geochemical vectoring of multiphase evolution of Au–Ag mineralization in the Rudabánya polymetallic complex, Hungary

Abstract

Supervised machine-learning code used to classify threshold-defined Au and Ag occurrences in the Rudabánya soil-geochemical dataset. The repository includes implementations of Random Forest, XGBoost, and Support Vector Machine models, together with preprocessing, class balancing, model evaluation, and feature-importance analysis. The code supports reproducibility of the supervised modelling workflow presented in the associated study.