Bagging vs. Boosting: The Power of Ensemble Methods in Machine Learning

How to maximize predictive performance by creating a strong learner from multiple weak ones

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Complex problems are rarely solved through singular thought or action. A collective weather forecast produced by a team of meteorologists, each with a different perspective, will likely result in better predictions than that produced by a single individual. Similarly, the world of machine learning finds power in ensemble methods — combining multiple models to improve predictions and, subsequently, decision-making.

With respect to ensemble learning, two strategies stand out: bagging and boosting. Both are powerful methods that have revolutionized the way we train our machine-learning models.

In this article, we’ll delve into the foundational concepts of these two methods and touch on some of the commonly used algorithms. But before we start our exploration, we need to first talk about a concept that’s central to ensemble learning, particularly to bagging: bootstrapping.

Bootstrapping

In the context of ensemble learning, bootstrapping refers to a sampling method that involves drawing random samples from a dataset with replacement. In other words, we create multiple…