What is feature importance in Random Forest?
Feature importance in Random Forest refers to the technique used to determine the significance of each feature in predicting the target variable.
How is feature importance calculated in Random Forest?
It is typically calculated using metrics like Mean Decrease Impurity (MDI) or Mean Decrease Accuracy (MDA) based on the model's performance.
Can feature importance help in feature selection?
Yes, it can help identify which features contribute the most to the model, allowing for more efficient feature selection.
Are there any limitations to using feature importance?
Yes, feature importance can be misleading in cases of correlated features, as it may not accurately reflect their individual contributions.
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