Comparar métodos

	Gradient Boosting	Regressão Quantílica
Área	Aprendizado de máquina	Econometria
Família	Machine learning	Regression model
Ano de origem	2001	1978
Autor original	Friedman, J. H.	Koenker & Bassett
Tipo	Ensemble (sequential boosting of decision trees)	Conditional quantile regression
Fonte seminal	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Outros nomes	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	conditional quantile regression, regression quantiles, Kantil Regresyon
Relacionados	5	5
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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.