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Related Questions
- How does one-hot encoding affect the interpretability of feature permutation importance in machine learning models?
- Can you explain the difference in feature permutation importance between one-hot encoded categorical features and label encoded categorical features?
- What is the impact of one-hot encoding on the handling of missing values in categorical features during feature permutation importance calculations?
- How does the interaction between one-hot encoding and feature selection methods affect the accuracy of feature permutation importance calculations?
- Can you provide an example of how one-hot encoding can lead to inflated feature permutation importance for categorical features?
- What are some common pitfalls to avoid when using one-hot encoding for categorical features in feature permutation importance calculations?
- How does the choice of encoding method (one-hot vs label encoding) impact the stability of feature permutation importance calculations in the presence of noisy data?
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