tsitsimis/tinyshap
Python package providing a minimal implementation of the SHAP algorithm using the Kernel method
This helps data scientists and machine learning engineers understand why a predictive model makes a certain forecast. You input your trained machine learning model and the data you want to explain, and it outputs numerical values showing how much each input feature contributed to that specific prediction. This is for data science practitioners who need to peek inside the "black box" of their models.
No commits in the last 6 months. Available on PyPI.
Use this if you want to learn the fundamental mechanics of SHAP (SHapley Additive exPlanations) for model interpretability in a simplified, easy-to-understand implementation.
Not ideal if you need a robust, high-performance solution for explaining predictions in a production environment with complex models or large datasets.
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Jupyter Notebook
License
MIT
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Last pushed
May 27, 2023
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