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Related Questions
- What are the key differences between feature permutation importance and SHAP values in model interpretability?
- How do feature permutation importance and SHAP values handle non-linear relationships between features and target variables?
- Can you explain how feature permutation importance and SHAP values are used in conjunction with other model interpretability techniques, such as partial dependence plots?
- How do feature permutation importance and SHAP values account for interactions between multiple features?
- What are the limitations of feature permutation importance and SHAP values in terms of their ability to capture complex relationships?
- Can you provide examples of when feature permutation importance and SHAP values are more suitable than other model interpretability techniques, and vice versa?
- How do feature permutation importance and SHAP values compare in terms of computational efficiency and scalability for large datasets?
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