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Striders is a lightning-fast, surrogate-based model explanations (XAI). It provides an efficient alternative to traditional SHAP by leveraging landmark-based kernel approximations. Striders implements a landmark-based approximation of the Shapley Kernel. By selecting representative landmarks, it reduces the complexity of the explanation process while maintaining high correlation with the true Shapley values.

Installation

pip install striders

Performance Benchmarking

Dataset (Task) Samples / Features Metric TreeSHAP Striders Speed-up
CA Housing (Reg.) 20,640 / 8 Execution Time 22.1948s 0.3927s 56.5x 🚀
Fidelity ($R^2$) - 0.9081
Correlation - 0.9490
Credit Default (Clf.) 30,000 / 23 Execution Time 47.0008s 2.4718s 19.0x 🚀
Fidelity ($R^2$) - 0.9776
Correlation - 0.9429

Reproducibility: You can run directly in: Open In Colab

Acknowledgments & Citations

This is an unofficial implementation based on the principles described in:

@article{ko2025stride,
  title={STRIDE: Subset-Free Functional Decomposition for XAI in Tabular Settings},
  author={Ko, Chaeyun},
  journal={arXiv preprint arXiv:2509.09070},
  year={2025}
}

If you find this implementation useful in your work, please consider citing this repository:

@software{striders2026,
  author={RektPunk},
  title={Striders: A High-Performance Rust-based Implementation of STRIDE},
  year={2026},
  url={https://github.com/RektPunk/striders},
}

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