Σύγκριση μεθόδων

	Ενισχυμένη Βαθμιδωτή Μάθηση Συνόλου (Ensemble Gradient Boosting)	AdaBoost
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2001	1997
Δημιουργός	Friedman, J. H.	Freund, Y. & Schapire, R.E.
Τύπος	Ensemble (sequential boosting of decision trees)	Ensemble (sequential boosting of weak learners)
Θεμελιώδης πηγή	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Εναλλακτικές ονομασίες	Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma
Συναφείς	6	5
Σύνοψη	Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.