Bootstrap aggregating, also called bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of classification and regression algorithms by reducing their variance and tendency to overfit. It is most commonly applied to decision tree methods but can be used with any type of underlying method, and it is considered a special case of the broader ensemble averaging approach.
Connections
Associated With
Both are the two dominant general ensemble-learning paradigms in the machine-learning literature: bagging combines base learners trained in parallel on bootstrap-resampled data, gradient boosting builds an ensemble sequentially, each stage correcting the previous stage's residual error.
Predecessor Of
Leo Breiman introduced bootstrap aggregating (bagging) in 1996 and built directly on it in his 2001 random forest algorithm.
Source Wikipedia: Random Forest Algorithm
Sources
Wikipedia: Random Forest Algorithm
Wikimedia FoundationPredecessor Of: Random Forest Algorithm, Bagging sectionQuote, Predecessor Of: Random Forest Algorithm, Bagging section
The training algorithm for random forests applies the general technique of bootstrap aggregating, or bagging, to tree learners.
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