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Fast TreeSHAP: Accelerating SHAP Value Computation for Trees
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SHAP (SHapley Additive exPlanation) values are one of the leading tools for interpreting machine learning models, with strong theoretical guarantees (consistency, local accuracy) and a wide availability of implementations and use cases. Even though computing SHAP values takes exponential time in general, TreeSHAP takes polynomial time on tree-based models. While the speedup is significant, TreeSHAP can still dominate the computation time of industry-level machine learning solutions on datasets with millions or more entries, causing delays in post-hoc model diagnosis and interpretation service. In this paper we present two new algorithms, Fast TreeSHAP v1 and v2, designed to improve the computational efficiency of TreeSHAP for large datasets. We empirically find that Fast TreeSHAP v1 is 1.5x faster than TreeSHAP while keeping the memory cost unchanged. Similarly, Fast TreeSHAP v2 is 2.5x faster than TreeSHAP, at the cost of a slightly higher memory usage, thanks to the pre-computation of expensive TreeSHAP steps. We also show that Fast TreeSHAP v2 is well-suited for multi-time model interpretations, resulting in as high as 3x faster explanation of newly incoming samples.
Forward citations
Cited by 6 Pith papers
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Shapley values in product games equal an exact one-dimensional integral of a polynomial, computable via Gauss-Legendre quadrature with linear cost in the number of features.
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QuadraSHAP: Stable and Scalable Shapley Values for Product Games via Gauss-Legendre Quadrature
Shapley values in product games equal the integral of a degree-(d-1) polynomial over [0,1], allowing provably exact or near-exact computation via Gauss-Legendre quadrature with O(d m_q) work.
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WOODELF-HD: Efficient Background SHAP for High-Depth Decision Trees
WoodelfHD reduces Background SHAP preprocessing for decision trees from 3^D to 2^D complexity, enabling exact computation on depths up to 21 with reported speedups of 33x to 162x.
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Rethinking XAI Evaluation: A Human-Centered Audit of Shapley Benchmarks in High-Stakes Settings
In high-stakes settings, Shapley explanations increase analyst confidence but do not improve decision accuracy, and standard metrics fail to predict human utility.
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From Decision Trees to Boolean Logic: A Fast and Unified SHAP Algorithm
WOODELF computes Background SHAP for tree ensembles in linear time via pseudo-Boolean formulas that encode trees, features, and background data, with reported speedups of 16x on CPU and 165x on GPU for million-row datasets.
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