Аңдатпа
Mineral prospectivity mapping (MPM) increasingly relies on machine learning classifiers, but model performance may be overstated when spatial dependence, extrapolation beyond the training domain, and poorly calibrated prediction scores are not adequately addressed. This study presents a reproducible workflow for regional Au-Ag prospectivity mapping in Nevada that integrates positive–unlabeled learning with Random Forest models and spatial GroupKFold cross-validation. Predictions were restricted to the Area of Applicability (AOA), defined using the 0.95 quantile of a training-derived feature-space dissimilarity index, resulting in areal coverage of 54.331% for Ag and 73.118% for Au. Discrimination based on pooled out-of-fold (OOF) predictions was strong for both commodities (Ag:AP = 0.745, ROC-AUC = 0.908; Au:AP = 0.777, ROC-AUC = 0.912). Post hoc OOF calibration with isotonic regression reduced Brier scores and improved probability reliability within the supported prediction domain. Area-based validation showed that the highest-ranked 10% of the AOA captured 85.6% of the Ag and 84.2% of the Au pooled-OOF positives, corresponding to targeting efficiencies of 8.6-fold and 8.4-fold relative to random spatial selection. Ensemble dispersion was used to map predictive uncertainty and to identify areas where additional data may be most useful for reducing uncertainty. SHAP importance and partial-dependence diagnostics consistently highlighted proximity to Quaternary faults, elevated slip and dilation tendency, and potential-field edges associated with intrusive or volcanic contacts, while surface heat flow acted mainly as a permissive control. The resulting AOA-masked prospectivity maps, uncertainty layers, and top-k targeting products provide a transparent regional framework for prioritizing follow-up exploration and allocating reconnaissance effort.