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Related Questions
- Can feature importance methods, like SHAP or LIME, capture non-linear interactions between input features?
- How do partial dependence plots, like those used in TreeExplainer or PDP, handle multi-modal relationships between input features and the predicted output?
- In machine learning models with non-linear relationships, how do we choose between feature importance and partial dependence plots for interpreting model behavior?
- Do model-agnostic interpretability methods, like Anchors or LFI, account for non-linear interactions between input features, and if so, how?
- In models with complex non-linear relationships, how do interpretability methods, like kernel SHAP or kernel partial dependence plots, handle kernel functions?
- What are the limitations of feature importance methods in capturing non-linear interactions between input features, and how can these be addressed?
- Can model-agnostic interpretability methods, such as partial dependence plots, be extended to handle high-dimensional data with many input features?
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