Logo Logo
Help
Contact
Switch Language to German

Muschalik, Maximilian ORCID logoORCID: https://orcid.org/0000-0002-6921-0204; Fumagalli, Fabian ORCID logoORCID: https://orcid.org/0000-0003-3955-3510; Hammer, Barbara ORCID logoORCID: https://orcid.org/0000-0002-0935-5591 and Hüllermeier, Eyke ORCID logoORCID: https://orcid.org/0000-0002-9944-4108 (February 2024): Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. AAAI Conference on Artificial Intelligence 2024, Vancouver, Canada, 20-27 February 2024. Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38, No. 13 pp. 14388-14396

Full text not available from 'Open Access LMU'.

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.

Actions (login required)

View Item View Item