shap and FastTreeSHAP
FastTreeSHAP is a specialized accelerator for the general-purpose SHAP library, optimizing Shapley value computation specifically for tree-based models that SHAP already supports.
About shap
shap/shap
A game theoretic approach to explain the output of any machine learning model.
This tool helps data scientists and machine learning engineers understand why their machine learning models make specific predictions. By taking a trained model and input data, it shows how much each individual feature contributes to the final output, clarifying complex model behavior. It's designed for anyone building or using ML models who needs to explain their results, like a business analyst evaluating a credit risk model or a medical researcher interpreting a diagnostic tool.
About FastTreeSHAP
linkedin/FastTreeSHAP
Fast SHAP value computation for interpreting tree-based models
This project helps data scientists, machine learning engineers, and researchers quickly understand why their tree-based models (like Random Forest or XGBoost) make specific predictions. It takes your trained tree model and a dataset, then efficiently calculates 'SHAP values' that show how much each input feature contributed to each prediction. This allows you to explain complex model behaviors in an understandable way, especially for large datasets.
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