手法を比較

	半教師あり バギング	勾配ブースティング
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2000s	2001
提唱者	Various (Breiman bagging + semi-supervised extensions, 1990s–2000s)	Friedman, J. H.
種類	Semi-supervised ensemble (bagging variant)	Ensemble (sequential boosting of decision trees)
原典	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.
別名	SS-Bagging, semi-supervised bootstrap aggregating, self-training bagging, bagging with pseudo-labels	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
関連	4	5
概要	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.