qshap 0.1.0

Exact computation of shapley R-squared in polynomial time

## Q-SHAP: Feature-Specific $R^2$ Values for Tree Ensembles This package is used to compute feature-specific $R^2$ values, following Shapley decomposition of the total $R^2$, for tree ensembles in polynomial time.

This version only takes output from XGBoost, scikit-learn Decision Tree, and scikit-learn GBDT. We are working to update it for random forest in the next version. Please check Q-SHAP Tutorial.ipynb for more usage.

### Citation `bibtex @article{jiang2024feature, title={Feature-Specific Coefficients of Determination in Tree Ensembles}, author={Jiang, Zhongli and Zhang, Dabao and Zhang, Min}, journal={arXiv preprint arXiv:2407.03515}, year={2024} } `

### References - Jiang, Z., Zhang, D., & Zhang, M. (2024). Feature-Specific Coefficients of Determination in Tree Ensembles. arXiv preprint arXiv:2407.03515. - Lundberg, Scott M., et al. “From local explanations to global understanding with explainable AI for trees.” Nature machine intelligence 2.1 (2020): 56-67. - Karczmarz, Adam, et al. “Improved feature importance computation for tree models based on the Banzhaf value.” Uncertainty in Artificial Intelligence. PMLR, 2022. - Bifet, Albert, Jesse Read, and Chao Xu. “Linear tree shap.” Advances in Neural Information Processing Systems 35 (2022): 25818-25828. - Chen, Tianqi, and Carlos Guestrin. “Xgboost: A scalable tree boosting system.” Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016.

### Container images We provide pre-built images with all necessary packages for Q-SHAP in Python 3.11, available for both Docker and Singularity:

• Docker: You can pull the Docker image using the following command: `sh docker pull catstat/xai `

• Singularity: You can pull the Docker image using the following command: `sh singularity pull docker://catstat/xai:0.0 `

### Task List

• [ ] Task 1: Lightgbm version

• [ ] Task 2: Catboost version