Exact Shapley Values for Local and Model-True Explanations of Decision Tree Ensembles
This work addresses the need for more precise and model-true explanations in machine learning interpretability, offering an incremental improvement over existing Shapley value methods for random forests and boosted trees.
The paper tackles the non-uniqueness of Shapley values in explaining decision tree ensembles by introducing a novel method that provides attributions accurately reflecting model prediction details for individual instances, achieving computational competitiveness with widely used methods.
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.