What is ensemble learning?

Can you explain what ensemble learning is?

Ensemble learning is a technique in machine learning where multiple models are combined to improve performance.

How does it work? What are the benefits?

It works by aggregating the predictions of several models, which can reduce errors and improve accuracy. Benefits include better generalization and robustness.

What are some common ensemble methods?

Common methods include bagging, boosting, and stacking. Each has its own approach to combining models.

Can you give an example of where ensemble learning is used?

Ensemble learning is often used in competitions like Kaggle, where combining models can lead to winning solutions.