Bagging vs Boosting in Machine Learning: What You Need to Know

If you're diving into machine learning, understanding bagging and boosting is key to building better models. Both are ensemble techniques—but they tackle problems differently.

Bagging (like in Random Forests) reduces variance by training multiple models in parallel on different data subsets and averaging the results. It's great for stabilizing high-variance models like decision trees and handling outliers well.

Boosting, on the other hand, focuses on reducing bias by training models sequentially, each one learning from the errors of the last. Algorithms like AdaBoost and XGBoost are powerful for improving accuracy, especially on complex datasets.

Both methods combine weak learners to build strong ones—but bagging is better for avoiding overfitting, while boosting aims for precision.

Whether you're working on classification, regression, or real-world projects, mastering these two can really level up your machine learning game.