What are ensemble methods in machine learning?

What are ensemble methods in machine learning?

Ensemble methods are techniques that combine multiple models to improve performance. They can reduce overfitting and increase accuracy.

Can you give me examples of ensemble methods?

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

How does Boosting work?

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

What libraries in Python can I use for ensemble methods?

You can use libraries like Scikit-learn, XGBoost, and LightGBM for implementing ensemble methods in Python.