SHAP

SHAP (SHapley Additive exPlanations) is a method used to explain the output of any machine learning model. It uses concepts from game theory to assign each feature a value representing its contribution to the prediction. These values, known as SHAP values, indicate the degree to which each feature contributed to the model's output for a specific instance, facilitating a deeper understanding of the model's decision-making process.

Visit the following resources to learn more:

• @official@Welcome to the SHAP documentation
• @opensource@shap
• @article@Explainable AI - Understanding and Trusting Machine Learning Models
• @article@When Shapley Values Break: A Guide to Robust Model Explainability
• @video@SHAP values for beginners | What they mean and their applications