השוואת שיטות

	גרדיאנט בוסטינג	רגרסיית ריבועים פחותים רגילים (OLS)	רגרסיית קוונטילים
תחום	למידת מכונה	אקונומטריקה	אקונומטריקה
משפחה	Machine learning	Regression model	Regression model
שנת המקור	2001	2019	1978
הוגה השיטה	Friedman, J. H.	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
סוג	Ensemble (sequential boosting of decision trees)	Linear regression	Conditional quantile regression
מקור מכונן	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	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
כינויים	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
קשורות	5	5	5
תקציר	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).	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.