Bagging vs boosting

Question

Bagging vs boosting

Accepted Answer

Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.

Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.
Boosting involves training multiple models sequentially, where each subsequent model corrects the errors made by the previous ones. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.
Bagging reduces variance and helps prevent overfitting, while boosting focuses on reducing bias and improving accuracy.
Examples of bagging algorithms include Random Forest and Bagged Decision Trees, while examples of boosting algorithms include AdaBoost and Gradient Boosting Machines.

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