# Bagging vs Boosting: Mastering Ensemble Learning in Machine Learning

When it comes to improving model accuracy, ensemble methods like bagging and boosting are total game changers. Bagging (like Random Forest) reduces variance by training multiple models in parallel on different subsets of data—great for high-variance models. Boosting (like AdaBoost, XGBoost) reduces bias by training models sequentially, each one fixing the errors of the last—perfect for high-bias scenarios.

Bagging is stable, simple, and handles noise well, while boosting is more complex but incredibly powerful for performance on tough tasks. Both approaches combine weak learners into a strong predictive model but in different ways.

Understanding these techniques is key if you're aiming to build high-performing, reliable models.

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