The operation of large-scale ore-forming processes triggers the development of neighboring mineral deposits of the same or related types in a metallogenic province. While these de-posits often bear striking similarities, variations in local geological settings cause differences in many deposit features. Therefore, in a metallogenic province, geochemical, geophysical, and geological signatures of local areas mineralized with a certain deposit type can show considerable inherent differences. The application of deposit-type locations as training sites, thus, introduces a type of stochastic uncertainty into data-driven mineral prospectivity mapping (MPM), impairing the predictive capability of this activity. This study delves into this type of uncertainty and applies an ensemble technique combining bootstrapping and naı¨ ve Bayes classifiers to measure this uncertainty and lessen its impact on the MPM-generated exploration targets. Two components, one representing the quantified uncertainty and the other a modulated predictive model, are retained by the proposed framework. This framework was applied to a suite of mineral-systems derived targeting criteria of skarn-type Cu mineralization in the Alborz–Azerbaijan magmatic belt of northern Iran. The predictive results derived by the proposed technique outperformed those derived using a single clas-sifier, showcasing its efficacy. In addition, a novel approach is described and applied to demarcating exploration targets marked by low uncertainty.
