Ensemble learning models with a Bayesian optimization algorithm for mineral prospectivity mapping

Abstract

Machine learning algorithms have been widely applied in mineral prospectivity mapping (MPM). In this study, we implemented ensemble learning of extreme gradient boosting (XGBoost) and random forest (RF) models to create MPM for magmatic hydrothermal tin polymetallic deposits in Xianghualing District, southern Hunan Province, China. Machine-learning models often require careful adjustment of the learning parameters and model hyperparameters for optimal global performance. However, parameter tuning often entails tedious calculations and sufficient expert experience, which is a time-consuming and labor-intensive process. To obtain the global optimal performance of the XGBoost and RF models, a Bayesian optimization algorithm (BOA) was employed with the aid of 5-fold cross validation to search for the most appropriate hyperparameters of the XGBoost and RF models. After the Bayesian optimization, the AUC values of both models were significantly improved, indicating that the BOA is a powerful optimization tool. The optimization results provide a reference for the empirical hyperparameter setting of ensemble learning models. Through a comparative study, the XGBoost model was shown to be superior to the RF model in terms of accuracy, precision, recall, F1 score, and kappa coefficient. In addition, the receiver operating characteristic curves and prediction–area curves showed that the XGBoost model outperformed the RF model, indicating that the XGBoost model had better prediction ability and stability in the case area. In this study, the XGBoost model shows great potential for MPM, offering a significant improvement over the BOA method.