SHapley Additive exPlanations or SHAP : What is it ?

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

SHAP (SHapley Additive exPlanations), by Cory Maklin

An Introduction to SHAP Values and Machine Learning Interpretability

From local explanations to global understanding with explainable AI for trees

Materials, Free Full-Text

9.6 SHAP (SHapley Additive exPlanations)

SHapley Additive exPlanations (SHAP)

Prediction of HHV of fuel by Machine learning Algorithm: Interpretability analysis using Shapley Additive Explanations (SHAP) - ScienceDirect

SHAP : A Comprehensive Guide to SHapley Additive exPlanations - GeeksforGeeks

Towards Explainable Artificial Intelligence in Financial Fraud Detection: Using Shapley Additive Explanations to Explore Feature Importance

Unraveling Model Predictions: A Deep Dive into SHAP (SHapley Additive exPlanations)

Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations - ScienceDirect

SHapley Additive exPlanations or SHAP : What is it ?

SHapley Additive exPlanation (SHAP) values (TreeExplainer) for the

8 Shapley Additive Explanations (SHAP) for Average Attributions