shap and cf-shap
B extends A by adding counterfactual reasoning to SHAP's feature importance explanations, making them complements rather than competitors—you would use B's counterfactual framework on top of A's core Shapley value implementation.
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 cf-shap
jpmorganchase/cf-shap
Counterfactual SHAP: a framework for counterfactual feature importance
When you need to understand why a machine learning model made a specific decision, this tool helps you find the most influential factors. It takes your trained model and its predictions as input, then identifies the 'counterfactual' changes that would alter the prediction, explaining how much each feature contributed. This is for data scientists and ML practitioners who build and deploy models and need to explain their behavior.
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