Ensemble methods are powerful techniques in machine learning that combine multiple models to improve performance. In this article, we’ll delve into two popular approaches: bagging, which reduces variance through averaging, and boosting, which enhances predictions by focusing on errors. Discover how each method works and when to use them effectively.