Bandingkan kaedah

	Bagging Separa-Seliaan	Peningkatan Cerun
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2000s	2001
Pengasas	Various (Breiman bagging + semi-supervised extensions, 1990s–2000s)	Friedman, J. H.
Jenis	Semi-supervised ensemble (bagging variant)	Ensemble (sequential boosting of decision trees)
Sumber perintis	Bennett, K. P., & Demiriz, A. (1999). Semi-supervised support vector machines. Advances in Neural Information Processing Systems, 11. MIT Press.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alias	SS-Bagging, semi-supervised bootstrap aggregating, self-training bagging, bagging with pseudo-labels	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Berkaitan	4	5
Ringkasan	Semi-supervised Bagging extends the classical bagging ensemble to settings where labeled training examples are scarce but large amounts of unlabeled data are available. Base learners trained on labeled data assign pseudo-labels to unlabeled examples; the expanded dataset is then used to grow a diverse ensemble whose aggregated vote is more accurate and more stable than any single model trained on the limited labeled set alone.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.