What are the advantages of ensemble methods over single models?

What are the advantages of ensemble methods over single models?

Ensemble methods combine multiple models to improve accuracy and robustness, reducing overfitting compared to single models.

Can you give me an example of an ensemble method?

Sure! Random Forest is a popular ensemble method that uses multiple decision trees to make predictions.

How do ensemble methods handle bias and variance?

Ensemble methods can reduce bias by combining weak learners and decrease variance by averaging predictions, leading to better generalization.

Are there situations where single models perform better?

Yes, single models can be more efficient and easier to interpret, especially in simpler problems or when computational resources are limited.

What are some common ensemble techniques?

Common techniques include Bagging, Boosting, and Stacking, each with its own approach to combining models.