Multi-Target Regression for Quality Prediction in a Mining Process

When multiple dependent variables exist in a regression model, this task is called as multi-target regression. In this case, a multi-output regressor is employed to learn the mapping from input features to output variables jointly. In this study, multi-target regression technique is implemented for quality prediction in a mining process to estimate the amount of silica and iron concentrates in the ore at the end of the process. In the experimental studies, different regressors that use Random Forest, AdaBoost, k-Nearest Neighbors and Decision Tree algorithms separately in the background were compared to determine the best model. Coefficient of determination (R
²
) measure was used as the evaluation metric. There are some studies that predict iron concentrate and silica concentrate separately. However, this paper provides a new contribution to the field by calculating these two values jointly since they have a great correlation.