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Related Questions
- Can feature permutation importance help identify features that are not contributing to the model's predictions?
- How can feature permutation importance be used to detect features that are highly correlated with each other?
- What are some potential consequences of having redundant features in a model?
- Can feature permutation importance be used to determine the minimum set of features required to achieve the same level of model performance?
- How does feature permutation importance compare to other methods for feature selection, such as mutual information or recursive feature elimination?
- Can feature permutation importance be used in conjunction with other feature selection methods to improve model interpretability?
- What are some real-world scenarios where feature permutation importance can be applied to improve model performance and reduce model complexity?
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