Bandingkan metode

	Peningkatan Gradien	Regresi Kuantil
Bidang	Pembelajaran Mesin	Ekonometrika
Keluarga	Machine learning	Regression model
Tahun asal	2001	1978
Pencetus	Friedman, J. H.	Koenker & Bassett
Tipe	Ensemble (sequential boosting of decision trees)	Conditional quantile regression
Sumber perintis	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.
Alias	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	conditional quantile regression, regression quantiles, Kantil Regresyon
Terkait	5	5
Ringkasan	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.