Difference between bagging and boosting approaches

Question

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 of its predecessor, with a focus on instances that were misclassified in previous iterations.
Examples of bagging algorithms include Random Forest and Bagging meta-estimator in scikit-learn.
Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.

Top TCS Assistant Manager interview questions & answers