(Precision, Recall, Accuracy) allows for a transparent assessment of risk before deploying the model for expensive greenfield drilling campaigns. 3.2. Second Stage Architecture selection. We executed 65 iterations were performed, evaluating 50 different architectures per iteration to find the optimal model using 100% of the data. 3.2.1. Machine Learning Architecture In the second, ML architecture was generated using all available information. A total of 65 different iterations were performed to arrive at an optimal architecture. These iterations included changes in variables and in the layers used. Additionally, each iteration involved a battery of 50 different ML architectures, which were ranked, and the top 5 performers were subsequently visualized. From the set of architecture identified in the first stage, the optimal one was selected and used to make predictions across the entire area. The metrics used were precision, recall, F1score, accuracy, and specificity. Evaluating 50 different architectures per iteration ensures that the final ML Architecture K-Neighbors selection is the most robust and stable tool for handling the specific noise and complexity of the district's geophysical and lithological data. 4. RESULTS AND DISCUSSION The model successfully predicted the mineralization of the targets. The selected ML Architecture was K-Neighbors, due to its ability to prioritize accuracy and minimize false positives. The mineralized lithological layers are present in all five blocks identified in the district. The study made it possible to identify new mineralized bodies, corroborate existing ones, and generate strategic knowledge for future exploration planning. The results of the first stage were successful, satisfactorily recognizing the Veta Pérez zone (Figure 3A). The resulting metrics were: 42.27% precision, 1.83% recall, 3.50% F1-score, 86.76% accuracy, and 99.62% specificity. The selected architecture was the one that maximized precision, prioritizing the reduction of false positives. After evaluating 50 different ML Architecture, KNeighbors stood out as the option that best met this criterion, offering an efficient solution aligned with the analysis objectives. 2A 2B
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