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

Two minutes NLP — Explain predictions with SHAP values, by Fabio Chiusano, NLPlanet

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

From local explanations to global understanding with explainable AI for trees

9.6 SHAP (SHapley Additive exPlanations)

Overview of SHapley Additive exPlanations (SHAP) values from all

Shapley Additive Explanations (SHAP)

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

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

Explaining Black Box Models: Ensemble and Deep Learning Using LIME and SHAP - KDnuggets

SHapley Additive exPlanations (SHAP)

SHAP: Shapley Additive Explanations - Arize AI

SHAP values for beginners What they mean and their applications

SHAP with Python (Code and Explanations)