LinkedIn Researchers Open-Source ‘FastTreeSHAP’: A Python Package That Enables An Efficient Interpretation of Tree-Based Machine Learning Models
Researchers from LinkedIn open-source the FastTreeSHAP package which is a Python module based on the paper ‘[Fast TreeSHAP: Accelerating SHAP Value Computation for Trees](https://arxiv.org/abs/2109.09847).’ Implementing the widely-used TreeSHAP algorithm in the SHAP package allows for the efficient interpretation of tree-based machine learning models by estimating sample-level feature significance values. Its package includes two new algorithms: FastTreeSHAP v1 and FastTreeSHAP v2, both of which improve TreeSHAP’s computational efficiency by taking a different approach. 

The empirical benchmarking tests show that FastTreeSHAP v1 is 1.5x faster than TreeSHAP while keeping memory costs the same, and FastTreeSHAP v2 is 2.5x faster while using slightly more memory. The FastTreeSHAP package fully supports parallel multi-core computing to speed up its computation.

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Paper: https://arxiv.org/pdf/2109.09847.pdf

Github: https://github.com/linkedin/fasttreeshap