qshap: Fast Shapley Decomposition of $R^2$ for Gradient-Boosted Trees
Numerous methods have been developed to quantify feature attributions in individual predictions for tree ensembles. However, many applications require global measures of feature contributions to overall model performance. Although local attribution scores can be aggregated to characterize feature importance, such summaries do not directly decompose measures of predictive performance, such as $R^2$. This article introduces qshap, available in both R and Python, which provides Shapley decomposition of $R^2$ values for gradient-boosted decision trees (GBDTs) to quantify feature-specific contributions to model performance. By decomposing the quadratic loss of individual observations, qshap pr...
| Authors | Zhongli Jiang, Min Zhang, Dabao Zhang |
|---|---|
| Primary category | stat.ML |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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