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Related Questions
- What is the difference between feature permutation importance and other feature selection methods like mutual information or recursive feature elimination?
- How does feature permutation importance compare to wrapper-based methods like forward or backward selection in terms of model interpretability and performance?
- Can feature permutation importance be used as a pre-processing step for wrapper-based methods to reduce the search space and improve efficiency?
- How does feature permutation importance interact with embedded feature selection methods like LASSO regression or elastic net regression?
- Can feature permutation importance be used to identify redundant or correlated features that may be removed using wrapper-based methods?
- How does feature permutation importance handle high-dimensional data and non-linear relationships between features and the target variable?
- Can feature permutation importance be used in conjunction with other feature selection methods to create an ensemble of selected features?
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