What are some applications of ensemble methods in machine learning?

What are some applications of ensemble methods in machine learning?

Ensemble methods combine multiple models to improve performance. Common applications include image classification, fraud detection, and recommendation systems.

Can you explain how ensemble methods improve accuracy?

Ensemble methods reduce overfitting and variance by averaging predictions from multiple models, leading to more robust results.

What are some popular ensemble techniques?

Popular techniques include Bagging, Boosting, and Stacking. Each has its own strengths and use cases.

How does Random Forest fit into ensemble methods?

Random Forest is a Bagging technique that builds multiple decision trees and merges their results for improved accuracy.

Are there any limitations to using ensemble methods?

Yes, ensemble methods can be computationally expensive and may require more data to train effectively.