Grouped Feature Importance and Combined Features Effect Plot
This work tackles the problem of interpretability for researchers and practitioners by extending existing methods to feature groups, though it is incremental as it builds on established techniques.
The paper addresses the need to interpret groups of features in machine learning models, providing a framework for assessing grouped feature importance and introducing a visualization technique for combined feature effects, with evaluation through simulation studies and a real-world example.
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.