SHapley Additive exPlanations, more commonly known as SHAP, is used to explain the output of Machine Learning models. It is based on Shapley values, which
A short history of SHAP - by Christoph Molnar
SHAP Values : The efficient way of interpreting your model, by BOUZIANE KHALID
8 Shapley Additive Explanations (SHAP) for Average Attributions
Interpretable machine learning with tree-based shapley additive explanations: application to metabolomics datasets for binary classification
Shapley Additive Explanations: Unveiling the Black Box of Machine Learning, by Everton Gomede, PhD
Frontiers Integration of shapley additive explanations with
Explain Machine Learning Model using SHAP – Machine Learning Geek
Shapley Additive Explanations (SHAP)
Shapley Additive Explanations (SHAP)
From local explanations to global understanding with explainable AI for trees
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Two minutes NLP — Explain predictions with SHAP values, by Fabio Chiusano, NLPlanet
SHapley Additive exPlanation (SHAP) values (TreeExplainer) for the
A comparison of explainable artificial intelligence methods in the
Towards Explainable Artificial Intelligence in Financial Fraud Detection: Using Shapley Additive Explanations to Explore Feature Importance
