Σύγκριση μεθόδων

	Ενίσχυση Κλίσης (Gradient Boosting)	Ενισχυμένη Ενίσχυση (Regularized Boosting)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2001	2001–2016
Δημιουργός	Friedman, J. H.	Friedman, J. H.; extended by Chen & Guestrin
Τύπος	Ensemble (sequential boosting of decision trees)	Regularized ensemble (boosting with shrinkage/penalty)
Θεμελιώδης πηγή	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Εναλλακτικές ονομασίες	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting
Συναφείς	5	5
Σύνοψη	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.	Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks.