What are ensemble models in machine learning?

What are ensemble models in machine learning?

Ensemble models combine multiple machine learning algorithms to improve performance. They can reduce overfitting and increase accuracy.

Can you give me examples of ensemble methods?

Sure! Common ensemble methods include Bagging, Boosting, and Stacking. Random Forest is a popular example of Bagging.

How does Boosting work?

Boosting works by combining weak learners sequentially, where each new model focuses on the errors made by the previous ones.

What are the advantages of using ensemble models?

Ensemble models often achieve better accuracy, robustness, and generalization compared to individual models.

Are there any disadvantages to using them?

Yes, ensemble models can be more complex and computationally expensive, and they may require more tuning.