Comparer des méthodes

	Forêt aléatoire semi-supervisée	Gradient Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2009	2001
Auteur d'origine	Leistner, C., Saffari, A., Santner, J., & Bischof, H.	Friedman, J. H.
Type	Semi-supervised ensemble classifier	Ensemble (sequential boosting of decision trees)
Source fondatrice	Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alias	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Apparentées	3	5
Résumé	Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation.	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.