Comparar métodos

	Gradient Boosting	Propagação de Rótulos	Random Forest
Área	Aprendizado de máquina	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning	Machine learning
Ano de origem	2001	2002	2001
Autor original	Friedman, J. H.	Zhu, X. & Ghahramani, Z.	Breiman, L.
Tipo	Ensemble (sequential boosting of decision trees)	Graph-based semi-supervised classification	Ensemble (bagging of decision trees)
Fonte seminal	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Outros nomes	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relacionados	5	3	4
Resumo	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.	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.