Minimal implementation of approximate Kernel SHAP algorithm
Project description
tinyshap
A minimal implementation of the SHAP algorithm using the KernelSHAP method. In less then 100 lines of code, this repo serves as an educational resource to understand how SHAP works without all the complexities of a production-level package.
Note: For reliable and more flexible estimation of SHAP values the user should use shap or a similar package.
Installation
pip install tinyshap
Example usage
from tinyshap import SHAPExplainer
# Train model
model = GradientBoostingRegressor()
model.fit(X_train, y_train)
# Explain predictions
explainer = SHAPExplainer(model.predict, X=X_train.mean().to_frame().T)
contributions = explainer.shap_values(X)
See complete notebook
Resources
Licence
MIT
Project details
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tinyshap-0.0.3.tar.gz
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