Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

• Published in: Ore geology reviews Vol. 71; pp. 804 - 818
• Main Authors: Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., Chica-Rivas, M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2015
• Subjects: Data-driven modelling; Hyperion; Machine learning; Mineral potential; Mineral prospectivity mapping; Mineral potential; Mineral prospectivity mapping; Data-driven modelling; Hyperion; Machine learning

Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), regression trees (RTs), random forest (RF) and support vector machines (SVMs) are powerful data driven methods that are relatively less widely used in the mapping of mineral prospectivity, and thus have not been comparativ...