What is feature importance in decision forests?

What is feature importance in decision forests?

Feature importance in decision forests refers to techniques that rank the influence of each feature on the model's predictions.

How is feature importance calculated in decision forests?

It's typically calculated using metrics like Gini importance or Mean Decrease Impurity, which assess how much each feature contributes to reducing uncertainty.

Can feature importance help in feature selection?

Yes, it can help identify the most relevant features, allowing for more efficient models by removing less important ones.

Are there any limitations to using feature importance?

Yes, feature importance can sometimes be misleading, especially in correlated features, as it may not capture the true interactions.

What are some applications of feature importance in real-world scenarios?

Applications include finance for credit scoring, healthcare for disease prediction, and marketing for customer segmentation.