In traditional GLM models interactions between predictors must be carefully isolated and inserted manually. In contrast, the non-parametric nature of many machine learning algorithms means that all interactions can be included by default. Beyond simply using these models for predictions, we have a suite of tools to analyse the interactions present in those models, which can be used as pointers to refine and improve traditional models.