This paper introduces TreeSHAP-IQ, an efficient method for computing any-order additive Shapley interactions for tree-based models. TreeSHAP-IQ extends the TreeSHAP methodology, which computes exact Shapley values for tree-based models, to any-order Shapley interactions. The method is based on a mathematical framework that uses polynomial arithmetic to compute interaction scores in a single recursive traversal of the tree, similar to Linear TreeSHAP. 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. The method is applied to state-of-the-art tree ensembles and used to explore interactions on well-established benchmark datasets. The paper also discusses the theoretical foundation of TreeSHAP-IQ, including its application to the broad class of Cardinal Interaction Indices (CIIs). The method is shown to be efficient and effective in computing any-order Shapley interactions for tree-based models, providing insights into feature interactions that are not captured by traditional Shapley values. The paper also discusses the limitations of the Shapley value in capturing interactions and the importance of considering interactions in model explanations. The results show that TreeSHAP-IQ provides a more comprehensive understanding of model predictions by capturing the interactions between features, which is essential for explaining complex models. The paper concludes that TreeSHAP-IQ is a valuable tool for explaining tree-based models and that further research is needed to explore its applications in various domains.This paper introduces TreeSHAP-IQ, an efficient method for computing any-order additive Shapley interactions for tree-based models. TreeSHAP-IQ extends the TreeSHAP methodology, which computes exact Shapley values for tree-based models, to any-order Shapley interactions. The method is based on a mathematical framework that uses polynomial arithmetic to compute interaction scores in a single recursive traversal of the tree, similar to Linear TreeSHAP. 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. The method is applied to state-of-the-art tree ensembles and used to explore interactions on well-established benchmark datasets. The paper also discusses the theoretical foundation of TreeSHAP-IQ, including its application to the broad class of Cardinal Interaction Indices (CIIs). The method is shown to be efficient and effective in computing any-order Shapley interactions for tree-based models, providing insights into feature interactions that are not captured by traditional Shapley values. The paper also discusses the limitations of the Shapley value in capturing interactions and the importance of considering interactions in model explanations. The results show that TreeSHAP-IQ provides a more comprehensive understanding of model predictions by capturing the interactions between features, which is essential for explaining complex models. The paper concludes that TreeSHAP-IQ is a valuable tool for explaining tree-based models and that further research is needed to explore its applications in various domains.