AI engineering glossary · Classical ML algorithms
What is bagging?
Bagging (also called Bootstrap aggregating): A method that trains many models on different random samples of the data, drawn with replacement, and averages their predictions or lets them vote. The averaging cancels out much of each model's random variation, lowering variance without raising bias much.
Bagging: an example
Train 100 decision trees each on a resampled copy of 10,000 rows and average their outputs.
Related terms
More Classical ML algorithms terms
Where you learn it
Bagging is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.