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Related Questions
- What is the SHAP (SHapley Additive exPlanations) framework and how does it help in understanding feature interactions in machine learning models?
- Can you explain how SHAP values calculate the contribution of individual features to the model's output while considering their interactions?
- How does SHAP handle high-dimensional feature spaces and feature interactions in its value allocation process?
- What are the advantages of using SHAP over other techniques for feature importance analysis, particularly in terms of feature interaction?
- Can SHAP values be used to identify which features are most important for a specific subset of the data?
- How does SHAP handle non-linear interactions between features, and what techniques does it use to assign values to these interactions?
- Are SHAP values sensitive to the specific machine learning model being used, or are they model-agnostic?
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