Comparer des méthodes

	Gradient Boosting	Apprentissage semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001	1970s–2006 (formalized)
Auteur d'origine	Friedman, J. H.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Ensemble (sequential boosting of decision trees)	Learning paradigm
Source fondatrice	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	5	5
Résumé	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.	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.