Porovnat metody

	Polo-supervizované skládání (Semi-supervised Stacking Ensemble)	Gradient Boosting
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2000s–2010s	2001
Tvůrce	Combines Wolpert (1992) stacking with semi-supervised learning principles	Friedman, J. H.
Typ	Ensemble (stacked generalization with unlabeled data augmentation)	Ensemble (sequential boosting of decision trees)
Původní zdroj	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Další názvy	SSL stacking, semi-supervised stacked generalization, self-trained stacking, semi-supervised meta-learning ensemble	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Příbuzné	5	5
Shrnutí	Semi-supervised Stacking Ensemble extends the classic stacked generalization framework to settings where only a fraction of training examples carry labels. Base learners are first trained on labeled data, then used to assign pseudo-labels to unlabeled examples; the expanded dataset trains stronger base models whose out-of-fold predictions form the input to a meta-learner, yielding a two-tier ensemble that exploits both labeled and unlabeled structure.	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.