Comparer des méthodes

	Gradient Boosting	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Apprentissage automatique	Économétrie
Famille	Machine learning	Regression model
Année d'origine	2001	2019
Auteur d'origine	Friedman, J. H.	Wooldridge (textbook treatment); classical least squares
Type	Ensemble (sequential boosting of decision trees)	Linear regression
Source fondatrice	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	5	5
Résumé	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).