Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
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| Format: | Preprint |
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2024
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| _version_ | 1866913397717598208 |
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| author | Muschalik, Maximilian Fumagalli, Fabian Hammer, Barbara Hüllermeier, Eyke |
| author_facet | Muschalik, Maximilian Fumagalli, Fabian Hammer, Barbara Hüllermeier, Eyke |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12069 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles Muschalik, Maximilian Fumagalli, Fabian Hammer, Barbara Hüllermeier, Eyke Machine Learning 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. |
| title | Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.12069 |