Explainable AI (XAI) Technique: SHAP (SHapley Additive exPlanations)

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SHAP (SHapley Additive exPlanations) is a popular technique in explainable AI (XAI) that provides a unified measure of feature importance for model predictions. It is based on cooperative game theory and the concept of Shapley values, which assign a value to each feature indicating its contribution to a particular outcome. SHAP values aim to fairly distribute the contribution of each feature to the prediction across all possible combinations of features.

I. Key Concepts:

1. Shapley Values:

• Shapley values come from cooperative game theory and provide a fair way to distribute the total contribution of each player in a game to the overall outcome. In the context of machine learning, features are considered players, and the prediction is the outcome.

2. Shapley Additive exPlanations (SHAP):

• SHAP values extend Shapley values to machine learning models, providing a way to explain the output of any machine learning model by attributing the contribution of each feature to the prediction.

II. How to Interpret SHAP Values:

SHAP values offer a quantitative measure of the impact of each feature on a specific…