| Summary: | Machine learning provides solutions to a diverse range of problems in high-dimensional datasets in geosciences. However, machine learning is generally criticized for being an enigmatic black box as it focusses on results but ignores the processes. To address this issue, we used supervised decision boundary maps (SDBM) to visually illustrate and interpret the machine learning process. We constructed a SDBM to classify the ore genetics from 1551 trace element data of apatite in various types of deposits. Attribute-based visual explanation of multidimensional projections (A-MPs) was introduced to SDBM to further demonstrate the correlation between features and machine learning process. Our results show that SDBM explores the interpretability of machine learning process and the A-MPs approach reveals the role of trace elements in machine learning classification. Combining SDBM and A-MPs methods, we propose intuitive and accurate discrimination diagrams and the most indicative elements for ore genetic types. Our work provides novel insights for the visualization application of geo-machine learning, which is expected to be a powerful tool for high-dimensional geochemical data analysis and mineral deposit exploration.
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