In recent years, the use of Machine Learning (ML) in mining has grown significantly, particularly in applications related to grade prediction, metallurgical recovery, geological classification, and geometallurgical variables such as hardness, energy consumption, and throughput. However, one of the most important — and at the same time most underestimated — aspects in