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Related Questions
- What are the key differences between model-agnostic and model-specific interpretability methods?
- How do model-agnostic methods, such as SHAP and LIME, account for feature interactions and non-linear relationships?
- Can you explain how model-agnostic methods handle high-dimensional feature spaces and feature correlations?
- In what ways do model-agnostic methods provide a more generalizable understanding of feature importance compared to model-specific methods?
- How do model-agnostic methods, such as feature permutation importance, address the issue of feature importance being model-dependent?
- What are the trade-offs between model-agnostic methods and model-specific methods in terms of interpretability and model performance?
- Can you discuss the challenges of applying model-agnostic methods to complex machine learning models, such as deep neural networks?
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