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

Author(s): Thomas A Dorfer

Originally published on Towards AI.

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

Image by the Author.

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… Read the full blog for free on Medium.

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Published via Towards AI