Trees bagging vs boosting

Question

Accepted Answer

Trees bagging and boosting are ensemble learning techniques that use multiple decision trees, but with different approaches.

Bagging (Bootstrap Aggregating) involves training multiple decision trees independently on different subsets of the training data and then averaging the predictions. Examples include Random Forest.
Boosting involves training multiple decision trees sequentially, with each tree correcting the errors of its predecessor. Examples include AdaBoost and Gradient Boosting.
Bagging reduces variance and helps prevent overfitting, while boosting focuses on reducing bias and improving accuracy.
Both bagging and boosting are used to improve the performance of decision trees by combining multiple weak learners into a strong learner.

Popular interview questions of Jr. Data Scientist