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A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence |
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| Institution: | 1. Camborne School of Mines, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus, Penryn, Cornwall, TR10 9FE, UK;2. British Geological Survey, Environmental Science Centre, Keyworth, Nottinghamshire, NG12 5GG, UK;3. University of Nottingham, Nottingham Geospatial Institute, Innovation Park, Nottingham, NG7 2TU, UK;4. Geological Survey of Finland, P.O. Box 77, FI-96101, Rovaniemi, Finland |
| Abstract: | |
| Keywords: | Machine learning Mineral prospectivity modelling Mineral exploration Random Forest? Tungsten SW England |
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