Comparar métodos

	Extra Trees	XGBoost
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2006	2016
Autor original	Geurts, P.; Ernst, D.; Wehenkel, L.	Chen, T. & Guestrin, C.
Tipo	Ensemble (extremely randomized decision trees)	Ensemble (gradient-boosted decision trees)
Fonte seminal	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Outros nomes	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionados	5	5
Resumo	Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time.	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.