What are the advantages of ensemble methods over single models in AI?

What are the advantages of ensemble methods over single models in AI?

Ensemble methods combine multiple models to improve accuracy and robustness, reducing overfitting compared to single models.

Can you give me examples of popular ensemble methods?

Sure! Some popular ensemble methods include Random Forest, Gradient Boosting, and Bagging.

How do ensemble methods reduce overfitting?

They average the predictions of multiple models, which helps to smooth out noise and capture the underlying patterns better.

Are there situations where single models perform better?

Yes, single models can be more efficient and easier to interpret, especially in simpler tasks or when computational resources are limited.

What should I consider when choosing between ensemble methods and single models?

Consider factors like the complexity of the problem, available data, computational resources, and the need for interpretability.