Can you explain how SHAP works in machine learning?
Sure! SHAP, or SHapley Additive exPlanations, is a method to explain the output of machine learning models by assigning each feature an importance value.
How does SHAP calculate the importance of each feature?
SHAP uses cooperative game theory to determine the contribution of each feature by considering all possible combinations of features.
Is SHAP applicable to all types of models?
Yes, SHAP can be applied to any machine learning model, including tree-based models, neural networks, and linear models.
What are the advantages of using SHAP over other explanation methods?
SHAP provides consistent and interpretable results, and it satisfies properties like local accuracy and consistency, making it a robust choice.
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