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Related Questions
- Can you provide a numerical example of a dataset where feature permutation importance is better than recursive feature elimination for feature selection?
- How does the choice of algorithm (e.g., random forest, gradient boosting) affect the performance of feature permutation importance compared to recursive feature elimination?
- In what scenarios (e.g., high-dimensional data, correlated features) does feature permutation importance tend to outperform recursive feature elimination?
- Can you explain the theoretical differences between feature permutation importance and recursive feature elimination, and how these differences impact their performance?
- How can feature permutation importance be used in conjunction with recursive feature elimination to improve feature selection performance?
- Can you provide a real-world example of a dataset where feature permutation importance was used to outperform recursive feature elimination for feature selection?
- What are some common pitfalls or limitations of using feature permutation importance, and how can they be addressed?
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