mlsanityData to 7 Oct 2026

Linear TreeShap

arXiv 2209.08192 ↗First posted 16 Sep 2022cs.LGcited by 12

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Abstract (arXiv)

Decision trees are well-known due to their ease of interpretability. To improve accuracy, we need to grow deep trees or ensembles of trees. These are hard to interpret, offsetting their original benefits. Shapley values have recently become a popular way to explain the predictions of tree-based machine learning models. It provides a linear weighting to features independent of the tree structure. The rise in popularity is mainly due to TreeShap, which solves a general exponential complexity problem in polynomial time. Following extensive adoption in the industry, more efficient algorithms are required. This paper presents a more efficient and straightforward algorithm: Linear TreeShap. Like TreeShap, Linear TreeShap is exact and requires the same amount of memory.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 2 of its 22 references that are in the map. First posted on arXiv 16 Sep 2022.

Builds on

2 of 22 references.

References · 2

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2021Fast TreeSHAP: Accelerating SHAP Value Computation for Treescited by 28

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 8.

12 papers.

  1. 2025SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelismcited by 8
  2. 2025Tree Ensemble Explainability through the Hoeffding Functional Decomposition and TreeHFD Algorithmcited by 5
  3. 2023The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Researchcited by 4
  4. 2026TreeGrad-Ranker: Feature Ranking via O(L)-Time Gradients for Decision Treescited by 4
  5. 2024Fast Calculation of Feature Contributions in Boosting Treescited by 3
  6. 2026Adalina: Adaptive Linear Approximation for the Shapley Value and Beyondcited by 1
  7. 2026Quadrature-TreeSHAP: Depth-Independent TreeSHAP and Shapley Interactionscited by 1
  8. 2026ConfoundingSHAP: Quantifying confounding strength in causal inferencecited by 0
  9. 2026Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networkscited by 0
  10. 2026OperatorSHAP: Fast and Accurate Shapley Value Estimation for Neural Operatorscited by 0
  11. 2026Nonlinear Axiomatic Attribution for Cooperative Gamescited by 0
  12. 2026qshap: Fast Shapley Decomposition of R2 for Gradient-Boosted Treescited by 0