Σύγκριση μεθόδων

	Ενίσχυση	Ενισχυμένη Ενίσχυση (Regularized Boosting)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1990–1997	2001–2016
Δημιουργός	Schapire, R. E.; Freund, Y.	Friedman, J. H.; extended by Chen & Guestrin
Τύπος	Sequential ensemble (iterative reweighting)	Regularized ensemble (boosting with shrinkage/penalty)
Θεμελιώδης πηγή	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.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Εναλλακτικές ονομασίες	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting
Συναφείς	6	5
Σύνοψη	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.