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Related Questions
- Does feature permutation importance effectively distinguish between important and non-significant features in high-dimensional spaces?
- How does feature permutation importance handle the curse of dimensionality in high-dimensional datasets?
- Is feature permutation importance efficient at identifying interactions between variables across multiple features?
- Can feature permutation importance quantify the relationship between input attributes and model predictions, thus providing insight into relationships?
- What kind of challenges can arise when estimating partial dependence in high-dimensional data via feature permutation importance? Can you list some reasons.
- How reliable/accurate is the impact of each feature via 'feature permutation importance?' Consider the 'curse of dimensionality.'
- Can feature permutation importance aid in selecting the best performing subset of features among different models when dealing with extensive feature spaces? Provide instance.
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