qshap-package: Calculating Feature-Specific R-Squared Values for Boosting...

In qshap: Fast Calculation of Feature Contributions in Boosting Trees

Calculating Feature-Specific R-Squared Values for Boosting Trees

Description

The qshap package computes feature-specific R-squared values using Shapley decomposition of the total R-squared for boosting trees built in xgboost and lightgbm. It supports parallel computing.

Details

The package provides fast computation of feature importance through Shapley values for tree ensemble models. Main functions include:

• gazer(): Create a Q-SHAP explainer from a trained model

• rsq(): Calculate feature-specific R-squared values

• loss(): Calculate feature-specific loss contributions

• plot(): Visualize R-squared values

The method uses polynomial-time complexity for Shapley value calculation and includes built-in support for multi-core processing.

Author(s)

Steven He, Zhongli Jiang, Min Zhang, Dabao Zhang

References

Zhongli Jiang, Min Zhang, and Dabao Zhang. 2025. Fast calculation of feature contributions in boosting trees. In Proceedings of the Forty-First Conference on Uncertainty in Artificial Intelligence (UAI '25), Vol. 286. JMLR.org, Article 82, 1859–1875.

See Also

Useful links:

• https://github.com/catstats/Q-SHAP_R

• Report bugs at https://github.com/catstats/Q-SHAP_R/issues

Examples

library(xgboost)
set.seed(42)
n <- 100
p <- 100
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
y <- X[, 1] - X[, 2] + rnorm(n, sd = 0.2)
model <- xgboost(X, y, nrounds = 15, max_depth = 2, verbosity = 0, nthread = 1)
explainer <- gazer(model)
phi_rsq <- rsq(explainer, X, y)

qshap documentation built on Aug. 23, 2026, 5:11 p.m.