Ensemble Learning

Definition

Ensemble learning is a machine learning paradigm where multiple models, known as "weak learners," are trained to solve the same problem and combined to get better results. The main idea is that a diverse group of models is often better than any single model alone.

Why It Matters

Ensemble methods are some of the most powerful techniques in classical machine learning. They can produce highly accurate and robust models by combining the predictions of many less-accurate ones.

Contextual Example

Random Forest is an ensemble method. It builds hundreds of individual decision trees and makes a final prediction based on a "vote" from all of them. This is much more accurate than relying on a single, overfitted decision tree.

Common Misunderstandings

• The two main types of ensemble methods are "bagging" (like Random Forest) which trains models in parallel, and "boosting" (like Gradient Boosting) which trains models sequentially.
• The key is to use a diverse set of models that make different kinds of errors.

Related Terms