Comparar métodos

	Árvore de Decisão Semi-supervisionada	Gradient Boosting
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2000s	2001
Autor original	Various (Levin & Shapiro; Zhu & Goldberg lineage)	Friedman, J. H.
Tipo	Semi-supervised classifier / regressor	Ensemble (sequential boosting of decision trees)
Fonte seminal	Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Outros nomes	SSDT, semi-supervised tree induction, self-training decision tree, label-propagation tree	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Relacionados	4	5
Resumo	A Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve better accuracy than a fully supervised tree trained on the labeled subset alone, which is especially valuable when labeling is expensive or time-consuming.	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.