Comparar métodos

	Gradient Boosting	Árvore de Decisão	Random Forest
Área	Aprendizado de máquina	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning	Machine learning
Ano de origem	2001	1984	2001
Autor original	Friedman, J. H.	Breiman, Friedman, Olshen & Stone	Breiman, L.
Tipo	Ensemble (sequential boosting of decision trees)	Recursive partitioning (if-then rules)	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.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Outros nomes	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relacionados	5	5	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.