Feature interactions
Features may not influence predictions independently. For example, as discussed in Chapter 2, Key Concepts of Interpretability, determining obesity based solely on weight isn’t possible. A person’s height or body fat, muscle, and other percentages are needed. Models understand data through correlations, and features are often correlated because they are naturally related, even if they are not linearly related. Interactions are what the model may do with correlated features. For instance, a decision tree may put them in the same branch, or a neural network may arrange its parameters in such a way that it creates interaction effects. This also occurs in our case. Let’s explore this through several feature interaction visualizations.
SHAP bar plot with clustering
SHAP comes with a hierarchical clustering method (shap.utils.hclust) that allows for the grouping of training features based on the “redundancy” between any given...
