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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Use this if you need to explain individual model predictions in a way that shows what minimal changes to the input data would flip the prediction to a different outcome.
Not ideal if you are looking to reproduce the specific experiments and results from the associated research paper, as those are in a separate repository.
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HTML
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Apache-2.0
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Last pushed
Jul 06, 2023
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