What are the different types of ensemble methods in machine learning?

What are the different types of ensemble methods in machine learning?

Ensemble methods combine multiple models to improve performance. The main types are bagging, boosting, and stacking.

Can you explain bagging and how it works?

Bagging, or Bootstrap Aggregating, involves training multiple models on random subsets of the data and averaging their predictions.

What about boosting? How is it different from bagging?

Boosting sequentially trains models, each focusing on the errors of the previous one, which helps to reduce bias.

Are there any popular algorithms that use these methods?

Yes! Random Forest is a popular bagging algorithm, while AdaBoost and Gradient Boosting are well-known boosting algorithms.