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Related Questions
- Can permutation importance metrics be biased when dealing with categorical features with many classes?
- How do permutation importance metrics handle high-cardinality categorical features?
- Under what conditions might permutation importance metrics fail to accurately measure the importance of a feature with mixed-type data?
- Can permutation importance metrics be biased when features have different scales or units?
- How does the number of features affect the accuracy of permutation importance metrics for mixed-type features?
- Are there any specific scenarios where permutation importance metrics are less reliable for features with missing values?
- Can permutation importance metrics be skewed when dealing with features that have non-linear relationships with the target variable?
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