What is SHAP?

SHAP (SHapley Additive exPlanations) is a game-theoretic approach to explain the output of any machine learning model. It uses Shapley values from game theory to assign each feature a value representing its contribution to the prediction. SHAP values can help you understand which features are most important to your model and how they affect the model's output. It is commonly used for model interpretability and feature importance analysis in various applications like finance, healthcare, and image recognition.