Σύγκριση μεθόδων

	Ενίσχυση Κλίσης (Gradient Boosting)	Δενδρόμονο με κανονικοποίηση
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2001	1984
Δημιουργός	Friedman, J. H.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Τύπος	Ensemble (sequential boosting of decision trees)	Supervised learning (regularized tree)
Θεμελιώδης πηγή	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
Εναλλακτικές ονομασίες	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Συναφείς	5	6
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.	A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees.