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Related Questions
- What are some common issues with traditional feature importance methods like permutation importance and SHAP values in high-dimensional data?
- How do techniques like recursive feature elimination (RFE) and L1 regularization address feature importance evaluation in high-dimensional data?
- What is the role of dimensionality reduction methods like PCA and t-SNE in feature importance evaluation?
- Can you explain the concept of feature importance in the context of random forests and gradient boosting machines?
- How do methods like recursive feature elimination with cross-validation (RFECV) and sequential feature selection (SFS) improve feature importance evaluation?
- What are some limitations of using feature importance scores to evaluate model performance in high-dimensional data?
- Can you discuss the trade-offs between interpretability and model performance when evaluating feature importance in high-dimensional data?
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