  Interpretable machine learning has become a very active area of research due
to the rising popularity of machine learning algorithms and their inherently
challenging interpretability. Most work in this area has been focused on the
interpretation of single features in a model. However, for researchers and
practitioners, it is often equally important to quantify the importance or
visualize the effect of feature groups. To address this research gap, we
provide a comprehensive overview of how existing model-agnostic techniques can
be defined for feature groups to assess the grouped feature importance,
focusing on permutation-based, refitting, and Shapley-based methods. We also
introduce an importance-based sequential procedure that identifies a stable and
well-performing combination of features in the grouped feature space.
Furthermore, we introduce the combined features effect plot, which is a
technique to visualize the effect of a group of features based on a sparse,
interpretable linear combination of features. We used simulation studies and a
real data example from computational psychology to analyze, compare, and
discuss these methods.
