Сравнение методов

	Полуавтоматический градиентный бустинг	Градиентный бустинг
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2006–2010s	2001
Автор метода	Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature	Friedman, J. H.
Тип	Semi-supervised ensemble (self-training + gradient boosted trees)	Ensemble (sequential boosting of decision trees)
Основополагающий источник	Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.)	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Другие названия	pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Связанные	6	5
Сводка	Semi-supervised gradient boosting combines gradient boosted trees with self-training or pseudo-labeling to exploit large pools of unlabeled data alongside a small labeled set. An initial GBM fit on labeled data assigns confident predictions to unlabeled examples; those pseudo-labeled points are folded back into training and the model is re-boosted, iterating until convergence. This allows practitioners to harness cheap unlabeled data when labels are scarce or expensive.	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.