What are the main challenges in ensemble methods?

What are the main challenges in ensemble methods?

Ensemble methods combine multiple models to improve performance, but they face challenges like overfitting, computational cost, and model diversity.

Can you explain how overfitting occurs in ensemble methods?

Overfitting in ensemble methods happens when the combined model learns noise from the training data, leading to poor generalization on unseen data.

What can be done to mitigate overfitting in ensemble methods?

To mitigate overfitting, techniques like cross-validation, pruning, and using simpler base models can be effective.

Are there specific ensemble methods that are more prone to these challenges?

Yes, methods like bagging can be more prone to overfitting if the base models are too complex, while boosting can be sensitive to noisy data.