Machine Learning to Predict and Explain Complex Carbon Cycle Interactions

Machine learning models are often considered as being black boxes. This chapter shows ways to gain in-depth understanding of the interactions between predictor and response variables in these algorithms. Specifically, a recent study (see the first suggested reading) is used to illustrate some examples of the utility of machine learning models in the carbon science field, and outline several methods available for machine learning model interpretability.