Fast Calculation of Feature Contributions in Boosting Trees

Zhongli Jiang, Min Zhang, Dabao Zhang

Keywords: Explainable AI, Feature importance, Shapley Value, Tree ensemble

Abstract: Recently, several fast algorithms have been proposed to decompose predicted value into Shapley values, enabling individualized feature contribution analysis in tree models. While such local decomposition offers valuable insights, it underscores the need for a global evaluation of feature contributions. Although coefficients of determination ($R^2$) allow for comparative assessment of individual features, individualizing $R^2$ is challenged by the underlying quadratic losses. To address this, we propose Q-SHAP, an efficient algorithm that reduces the computational complexity of calculating Shapley values for quadratic losses to polynomial time. Our extensive simulations show that Q-SHAP not only improves computational efficiency but also enhances the accuracy of feature-specific $R^2$ estimates.

Code Link: https://github.com/catstats/Q-SHAP

Readers: auai.org/UAI/2025/Conference, auai.org/UAI/2025/Conference/Area_Chairs, auai.org/UAI/2025/Conference/Reviewers, auai.org/UAI/2025/Conference/Submission516/Authors, auai.org/UAI/2025/Conference/Submission516/Reproducibility_Reviewers

Submission Number: 516