Machine learning
Bagging (Bootstrap Aggregating)
Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.
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Sources
- Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI: 10.1007/BF00058655 ↗
- Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 8.7). Springer. ISBN: 978-0-387-84857-0
- James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 8.2). Springer. ISBN: 978-1-4614-7138-7
Related methods
Referenced by
Active Learning Voting EnsembleBayesian Stacking EnsembleBoostingEnsemble Apriori AlgorithmEnsemble Association RulesEnsemble Decision TreeEnsemble Federated LearningEnsemble Gaussian Mixture ModelEnsemble K-nearest neighborsEnsemble Linear RegressionEnsemble Naive BayesEnsemble Semi-supervised LearningEnsemble Support Vector MachineExplainable Voting EnsembleExtra TreesOnline BaggingRobust BaggingRobust Random ForestRobust Stacking EnsembleRobust Voting EnsembleVoting Ensemble