Bagging

Boosting

Decision Tree

Ensemble Technique

Machine Learning

January 13, 2021

How do boosting and bagging differ?

Following are differences between Bagging & boosting:

1. Like Bagging, Boosting also employs bootstrapping but the resampling in done is a strategic manner to ensure that the consecutive Decision Tree gets more informative training data.
2. There is a mathematical certainty that ensemble model built using Boosting would perform better than individual algorithms, using which it was built. No such thing can be said about bagging.
3. In Boosting sequential learning happens, while in bagging all the individual models work independently.
4. In Bagging equal weightage is given to output from all trees while Boosting algorithm gives gives preference to high performers.

by : Monis Khan