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- When dealing with high-dimensional data, feature permutation importance is more suitable due to its computational efficiency and ability to handle large numbers of features.
- In situations where the relationships between features are complex, feature permutation importance can capture interactions between features more effectively.
- When the goal is to understand the contribution of each feature to the model's predictions, feature permutation importance provides a more direct and interpretable measure.
- In cases where the dataset is imbalanced, feature permutation importance can help identify features that are more relevant to the minority class.
- When the data is highly correlated, feature permutation importance can help identify the most important features by permuting all features and selecting the ones that result in the largest decrease in model performance.
- In situations where the model is highly non-linear, feature permutation importance can help identify features that are important for the model's predictions even if they are not strongly correlated with the target variable.
- When the dataset is very large, feature permutation importance can be more efficient than recursive feature elimination as it only requires a single pass through the data.
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