What is ensemble learning in machine learning?

What is ensemble learning in machine learning?

Ensemble learning is a technique that combines multiple models to improve performance. It leverages the strengths of each model.

Can you give me some examples of ensemble learning methods?

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

How does boosting differ from bagging?

Boosting focuses on correcting the errors of previous models by giving more weight to misclassified instances, while bagging reduces variance by averaging predictions from multiple models.

When should I use ensemble learning?

Ensemble learning is beneficial when you want to improve accuracy, reduce overfitting, or when individual models perform poorly on their own.