What are ensemble techniques in machine learning?

What are ensemble techniques in machine learning?

Ensemble techniques combine multiple models to improve performance. Common methods include bagging, boosting, and stacking.

Can you explain how boosting works?

Boosting focuses on training models sequentially, where each new model corrects errors made by the previous ones.

What is the difference between bagging and boosting?

Bagging reduces variance by averaging predictions from multiple models, while boosting reduces bias by focusing on errors.

Can you give an example of an ensemble method in Python?

Sure! A popular example is using the Random Forest classifier from the scikit-learn library.