AUC 0.9338 0.9238 0.9823 0.9989 0.8560 0.8309 0.9730 Precision 0.0017 0 0 0.0036 0 0 0.9800 Recall 0.8750 0 0 1 0 0 0.5000 F1-score 0.0034 0 0 0.0700 0 0 0.6700 Threshol d 0.0930 0 0.0031 0.0040 0.0068 0.0101 0.7200 Wmd: Weighted Mean, RF: Random Forest, GAM: Generalized Additive Model, XGB: Extreme Gradient Boosting, LGB: Light Gradient Boosting Machine, KNN: K-Nearest Neighbors, SVR: Support Vector Regression. 4. DISCUSSION The results demonstrate the superiority of non-linear machine learning approaches over heuristic weighted combinations for mineral prospectivity mapping. While the empirical weighted mean failed to capture meaningful relationships (R² = 0.0045), SVR and XGB successfully modeled complex interactions among geological, geochemical, structural, and remote sensing variables. Similar improvements of ML over heuristic or linear methods have been reported in porphyry and gold prospectivity studies (Abedini et al., 2023; Mitra et al., 2025). The distinct performance profiles of SVR and XGB reveal complementary exploration strategies. SVR achieved the highest precision (0.98) and F1-score (0.6667), making it suitable for advanced exploration stages where minimizing false positives is critical. Its higher optimal threshold (0.72) reflects a conservative classification strategy that prioritizes confidence in predicted targets. In limited training scenarios, SVM-based approaches have shown sensitivity to overfitting but can perform robustly when carefully tuned (Lachaud et al., 2023). This behavior is particularly advantageous in capital-intensive drilling campaigns where false positives directly translate into elevated operational costs. In contrast, XGB achieved near-perfect ranking ability (AUC = 0.9989) and complete recall (1.00), ensuring that no known mineralized zones in the validation set were omitted. However, its low precision indicates that exhaustive anomaly detection increases false positives. This makes XGB more appropriate for early-stage regional screening, where sensitivity is prioritized over selectivity. Comparable high AUC values (>0.99) for XGB and RF have been reported in gold prospectivity studies (Mitra et al., 2025), though class imbalance effects and threshold sensitivity must be carefully interpreted. The elevated AUC values observed in both models can be partly attributed to the strong geological determinism of porphyry systems. Intrusive centers, hydrothermal alteration halos, structural corridors, and associated geochemical anomalies provide high signal-to-noise
RkJQdWJsaXNoZXIy MTM0Mzk2