  We offer a new formalism for global explanations of pairwise feature
dependencies and interactions in supervised models. Building upon SHAP values
and SHAP interaction values, our approach decomposes feature contributions into
synergistic, redundant and independent components (S-R-I decomposition of SHAP
vectors). We propose a geometric interpretation of the components and formally
prove its basic properties. Finally, we demonstrate the utility of synergy,
redundancy and independence by applying them to a constructed data set and
model.
