When trying to understand the difference between machine learning and statistics, it is important to note that it is not so much the set of techniques and theory that are used but more importantly the intended use of the results. In fact, many of the underpinnings of machine learning are statistical in nature. When considering statistics, the main intent of statistics is in gaining an understanding of the underlying system, in this case geospatial system, through an analysis of observations or data about the system. Here, the geostatistician or environmental modeller is interested in cause and effect in the underlying system and gaining a deeper understanding of system itself. As a result of the need for environmental modellers and geostatisticians to gain an understanding of the underlying system, it is important that the eventual statistical model be interpretable, that is, not a black box. In fact, one reason for the limited use of machine learning algorithms has historically been exactly the lack of interpretability.