Confronta i metodi

	Gradient Boosting	Regression with Ordinary Least Squares (OLS)
Campo	Apprendimento automatico	Econometria
Famiglia	Machine learning	Regression model
Anno di origine	2001	2019
Ideatore	Friedman, J. H.	Wooldridge (textbook treatment); classical least squares
Tipo	Ensemble (sequential boosting of decision trees)	Linear regression
Fonte seminale	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
Correlati	5	5
Sintesi	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).