Editing Bootstrap aggregating (section)

{{Short description|Method in machine learning}}
{{Machine learning|Supervised learning}}

'''Bootstrap aggregating''', also called '''bagging''' (from '''b'''ootstrap '''agg'''regat'''ing''') or '''bootstrapping''', is a [[machine learning]] (ML) [[Ensemble learning|ensemble]] [[meta-algorithm]] designed to improve the [[Stability (learning theory)|stability]] and accuracy of ML [[Statistical classification|classification]] and [[Regression analysis|regression]] algorithms. It also reduces [[variance]] and [[overfitting]]. Although it is usually applied to [[Decision tree learning|decision tree]] methods, it can be used with any type of method. Bagging is a special case of the [[Ensemble averaging (machine learning)|ensemble averaging]] approach.