  While shallow decision trees may be interpretable, larger ensemble models
like gradient-boosted trees, which often set the state of the art in machine
learning problems involving tabular data, still remain black box models. As a
remedy, the Shapley value (SV) is a well-known concept in explainable
artificial intelligence (XAI) research for quantifying additive feature
attributions of predictions. The model-specific TreeSHAP methodology solves the
exponential complexity for retrieving exact SVs from tree-based models.
Expanding beyond individual feature attribution, Shapley interactions reveal
the impact of intricate feature interactions of any order. In this work, we
present TreeSHAP-IQ, an efficient method to compute any-order additive Shapley
interactions for predictions of tree-based models. TreeSHAP-IQ is supported by
a mathematical framework that exploits polynomial arithmetic to compute the
interaction scores in a single recursive traversal of the tree, akin to Linear
TreeSHAP. We apply TreeSHAP-IQ on state-of-the-art tree ensembles and explore
interactions on well-established benchmark datasets.
