Comparar métodos

	Gradient Boosting Semi-supervisionado	XGBoost
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2006–2010s	2016
Autor original	Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature	Chen, T. & Guestrin, C.
Tipo	Semi-supervised ensemble (self-training + gradient boosted trees)	Ensemble (gradient-boosted decision trees)
Fonte seminal	Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.)	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Outros nomes	pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionados	6	5
Resumo	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.