Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles

• URL: http://arxiv.org/abs/2401.12069v1
• Date: Mon, 22 Jan 2024 16:08:41 GMT
• Title: Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
• Authors: Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke H\"ullermeier
• Abstract summary: The Shapley value (SV) is a concept in explainable artificial intelligence (XAI) research for quantifying additive feature attributions of predictions. We present TreeSHAP-IQ, an efficient method to compute any-order additive Shapley interactions for predictions tree-based models.
• Score: 6.664930499708017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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