ORCID: https://orcid.org/0000-0002-6921-0204; Fumagalli, Fabian
ORCID: https://orcid.org/0000-0003-3955-3510; Hammer, Barbara
ORCID: https://orcid.org/0000-0002-0935-5591 und Hüllermeier, Eyke
ORCID: 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
[PDF, 3MB]


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.
Item Type: | Conference or Workshop Item (Paper) |
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Faculties: | Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning |
Subjects: | 000 Computer science, information and general works > 004 Data processing computer science |
URN: | urn:nbn:de:bvb:19-epub-118342-9 |
ISSN: | 2159-5399 |
Language: | English |
Item ID: | 118342 |
Date Deposited: | 25. Jun 2024 05:55 |
Last Modified: | 26. Nov 2024 17:29 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 438445824 |