Glossary term
Glossary term
Foundations
A method to train an ensemble where each constituent model trains on a random subset of training examples sampled with replacement. For example, a random forest is a collection of decision trees trained with bagging.
The term bagging is short for bootstrap aggregating.
See Random forests in the Decision Forests course for more information.
Created for this library
A telecom analytics team uses bagging to reduce variance in its customer-churn ensemble across noisy regional datasets.
An insurance underwriting team applies bagging to a set of weak claim-likelihood models to produce a more stable production score.
A retail forecasting team uses bagging on bootstrap samples of weekly sales to produce a robust demand estimate per SKU.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License