What are ensemble methods in AI models?

What are ensemble methods in AI models?

Ensemble methods combine multiple models to improve performance and accuracy. They leverage the strengths of each model.

Can you give me some examples of ensemble methods?

Sure! Common examples include Bagging, Boosting, and Stacking. Each has its own approach to combining models.

How does Boosting work specifically?

Boosting works by sequentially training models, where each new model focuses on the errors made by the previous ones, improving overall accuracy.

What are the advantages of using ensemble methods?

Ensemble methods can reduce overfitting, improve predictive performance, and provide more robust results compared to single models.