The identification models of the copper recovery using supervised machine learning algorithms for the geochemical data

Elemental analysis of the mining exploration data is important for many geochemical applications. The objective of this work is to conduct a comparison analysis of machine learning algorithms in predicting recovery of copper (Cu) from the Kazakhstan field. The data of cores was measured using the portable X-ray fluorescence (XRF) and the laboratory devices. We focused on the supervised machine learning algorithms such as K nearest neighbors (kNN), Decision Trees, Random Forest, XGBoost and LightGBM. The cross-validation result of these models shows that the Random Forest method can be used in the accurate prediction of the Cu recovery based on traditional laboratory tests. The examination of the algorithms is performed by metrics such as root mean square error, R2, and MAPE. In addition, the evaluation metrics of XGBoost and LightGBM are close to result of the Random Forest. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework.