Bandingkan metode

	Bagging (Bootstrap Aggregating)	Boosting	Pembelajaran Semi-terawasi
Bidang	Pembelajaran Mesin	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	1996	1990–1997	1970s–2006 (formalized)
Pencetus	Breiman, L.	Schapire, R. E.; Freund, Y.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tipe	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Sequential ensemble (iterative reweighting)	Learning paradigm
Sumber perintis	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Terkait	5	6	5
Ringkasan	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.