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Related Questions
- What are the key differences between SHAP and LIME, and how do they assign feature importance in high-dimensional data?
- How do model-agnostic interpretability methods, such as feature permutation importance and partial dependence plots, compare to model-specific methods in terms of computational cost and interpretability?
- Can you explain how the LIME method works, and how it can be used to interpret feature importance in complex machine learning models?
- What are some common challenges and limitations of model-agnostic interpretability methods, and how can they be addressed?
- How do model-agnostic interpretability methods, such as SHAP and LIME, handle non-linear interactions between features, and what are the implications for interpreting feature importance?
- Can you compare and contrast the strengths and weaknesses of model-agnostic and model-specific interpretability methods for high-dimensional data?
- How can model-agnostic interpretability methods be used to identify feature importance in datasets with many correlated features, and what are the implications for model development and deployment?
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