What are the main challenges of ensemble methods in machine learning?

What are the main challenges of ensemble methods in machine learning?

Ensemble methods can face several challenges, including overfitting, computational cost, and model diversity. Would you like to explore any of these in detail?

Yes, can you explain how overfitting occurs in ensemble methods?

Overfitting in ensemble methods happens when the combined model becomes too complex, capturing noise instead of the underlying pattern. This often occurs if individual models are too complex or if there's insufficient data.

What can be done to mitigate overfitting in ensemble methods?

To mitigate overfitting, you can use techniques like cross-validation, pruning individual models, or using simpler base models. Regularization can also help.

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

Yes, methods like Random Forests can be prone to overfitting if the trees are too deep. Boosting methods, like AdaBoost, can also overfit if not properly tuned.

Thanks! This was really helpful.

You're welcome! If you have more questions about ensemble methods or anything else, feel free to ask!