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 Values : The efficient way of interpreting your model, by BOUZIANE KHALID

shapley-additive-explanations · GitHub Topics · GitHub

8 Shapley Additive Explanations (SHAP) for Average Attributions

Interpretable AI with SHAP. A case for interpretable AI, by Quin Daly

Shapley additive explanations for NO2 forecasting - ScienceDirect

A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP) - ScienceDirect

Shapley additive explanations (SHAP) analysis of the individual feature

SHAP (Shapley Additive Explanations) with caret in R - Stack Overflow

Understanding SHAP for Interpretable Machine Learning, by Chau Pham

Unified Approach to Interpret Machine Learning Model: SHAP + LIME

SHAP values for beginners What they mean and their applications

PDF] Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations

SHAP: Shapley Additive Explanations, by Fernando López