Pedometrics › Forums › 10 PM challenges › What can we learn about soil processes from calibrated machine learning models? › Reply To: What can we learn about soil processes from calibrated machine learning models?
[From Philippe Lagacherie, INRA, France]
I do not expect getting new insights on the soil cover by using machine learning methods at the scale they are usually applied. Machine learning methods make only predictions, often more accurate than those of other models when dealing with large areas that include a lot of soil systems with many drivers acting differently from a place to another. Yes, we need to open the black box, but it is more for checking that the soil predictions match well our current pedological knowledge. If they do, it is enough for making me happy. Any so-called new insight has more chances to be an overfit than anything else. If we want to learn something about soil variations, we should work differently: delineating first well-identified soil systems, collect enough data and use statistical or mechanistic models that are easier to interpret.