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
Shapley additive explanations (SHAP) summary plot showing how the
SHapley Additive exPlanations or SHAP : What is it ?
Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation ( SHAP) Methods: Paper and Code - CatalyzeX
Explainable Machine Learning, Game Theory, and Shapley Values: A
Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification
Complete Guide to SHAP - SHAPley Additive exPlanations for Practitioners
SHapley Additive exPlanations(SHAP): A Simple Explainer
A Comprehensive Guide into SHAP Values
Explain Machine Learning Model using SHAP – Machine Learning Geek
Prediction of HHV of fuel by Machine learning Algorithm: Interpretability analysis using Shapley Additive Explanations (SHAP) - ScienceDirect
PDF] Counterfactual Shapley Additive Explanations
Two minutes NLP — Explain predictions with SHAP values, by Fabio Chiusano, NLPlanet
Using SHAP values to explain and enhance Machine Learning models
