# Exact Shapley Values for Local and Model-True Explanations of Decision Tree Ensembles

arXiv: https://arxiv.org/abs/2112.10592 · HTML: https://mlsanity.com/paper/2112.10592/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Problems with Shapley-value-based explanations as feature importance measures](https://mlsanity.com/paper/2002.11097/) (2020)
- [Feature relevance quantification in explainable AI: A causal problem](https://mlsanity.com/paper/1910.13413/) (2019)
- [True to the Model or True to the Data?](https://mlsanity.com/paper/2006.16234/) (2020)
- [Shapley explainability on the data manifold](https://mlsanity.com/paper/2006.01272/) (2020)
- [Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance](https://mlsanity.com/paper/1905.03151/) (2019)
- [Shapley Flow: A Graph-based Approach to Interpreting Model Predictions](https://mlsanity.com/paper/2010.14592/) (2020)
- [On Locality of Local Explanation Models](https://mlsanity.com/paper/2106.14648/) (2021)

## Built on by (most cited first, 5 of 5)

- [The Inadequacy of Shapley Values for Explainability](https://mlsanity.com/paper/2302.08160/) (2023)
- [Provably Accurate Shapley Value Estimation via Leverage Score Sampling](https://mlsanity.com/paper/2410.01917/) (2024)
- [Stabilizing Estimates of Shapley Values with Control Variates](https://mlsanity.com/paper/2310.07672/) (2023)
- [A Refutation of Shapley Values for Explainability](https://mlsanity.com/paper/2309.03041/) (2023)
- [On marginal feature attributions of tree-based models](https://mlsanity.com/paper/2302.08434/) (2023)

## Abstract (arXiv)

Additive feature explanations using Shapley values have become popular for providing transparency into the relative importance of each feature to an individual prediction of a machine learning model. While Shapley values provide a unique additive feature attribution in cooperative game theory, the Shapley values that can be generated for even a single machine learning model are far from unique, with theoretical and implementational decisions affecting the resulting attributions. Here, we consider the application of Shapley values for explaining decision tree ensembles and present a novel approach to Shapley value-based feature attribution that can be applied to random forests and boosted decision trees. This new method provides attributions that accurately reflect details of the model prediction algorithm for individual instances, while being computationally competitive with one of the most widely used current methods. We explain the theoretical differences between the standard and novel approaches and compare their performance using synthetic and real data.