70 Figure 3 - Location of the study area. 2.2. Machine Learning Integration Workflow Spatial predictors derived from Fertility and Architecture were standardized and integrated using two supervised machine learning algorithms: Random Forest (RF) and Artificial Neural Networks (Multilayer Perceptron, MLP)Training labels were defined as: Class 01: Known porphyry–skarn deposits and Class 02: Absence of known mineralization.Model performance was evaluated using Precision, Recall, and F1-score metrics. The workflow for data preprocessing, rasterization, feature standardization, model training, and validation is illustrated in Figure 4. Figure 4 - Workflow diagram illustrating the application of the Random Forest and artificial neural network (multilayer perceptron, MLP) algorithms used in this study. Source: Marchena et al. (2025). To evaluate model robustness and reduce overfitting, k-fold cross-validation was implemented during the training stage. The dataset was randomly partitioned into k subsets, where each subset was iteratively used as validation data while the remaining k−1 subsets were used for training. Performance metrics, including Precision, Recall, and F1-score, were averaged across all validation iterations to assess model stability and generalization capacity. Because geological datasets commonly exhibit spatial autocorrelation, cross-validation results were interpreted cautiously. The adopted procedure evaluates statistical reproducibility within the available dataset but does not fully eliminate the possibility of spatial dependency between training and validation samples. Consequently, the prospectivity maps generated should be regarded as regional permissivity models rather than fully independent predictive forecasts.
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