Vinod Chugani compares two techniques for working with trees:
Ensemble learning techniques primarily fall into two categories: bagging and boosting. Bagging improves stability and accuracy by aggregating independent predictions, whereas boosting sequentially corrects the errors of prior models, improving their performance with each iteration. This post begins our deep dive into boosting, starting with the Gradient Boosting Regressor. Through its application on the Ames Housing Dataset, we will demonstrate how boosting uniquely enhances models, setting the stage for exploring various boosting techniques in upcoming posts.
Read on for more information. The neat part about the “boosting versus bagging” debate is that both techniques are quite useful. Although boosting (via algorithms like XGBoost or LightGBM) is the more popular technique, bagging (random forest) is extremely powerful in its own right.