In predictive analytics, how is feature importance determined in ensemble methods like Random Forest?
- It calculates the average importance score of each feature across all trees in the forest.
- It only considers the importance of the first few features.
- It randomly assigns importance scores to features.
- It relies on the order of features in the dataset.
Feature importance in ensemble methods like Random Forest is determined by calculating the average importance score of each feature across all trees in the forest. This aggregation provides a more robust measure of feature importance, helping to identify the most influential variables in making predictions.
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