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Related Questions
- What are some common permutation importance methods used in machine learning, and how do they differ in handling high-cardinality categorical features?
- How does the SHAP (SHapley Additive exPlanations) method handle high-cardinality categorical features, and what are its advantages and disadvantages in this context?
- Can you explain the difference between permutation feature importance and SHAP values in handling high-cardinality categorical features, and when would you choose one over the other?
- How does the permutation importance method affect the interpretability of high-cardinality categorical features, and what are some strategies for improving interpretability in this case?
- What are some common issues that arise when using permutation importance methods to handle high-cardinality categorical features, and how can they be addressed?
- Can you provide an example of how to use permutation importance methods to handle high-cardinality categorical features in a real-world dataset, and what are some best practices to keep in mind?
- How does the choice of permutation importance method impact the performance of a machine learning model when dealing with high-cardinality categorical features?
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