Módszerek összehasonlítása

	Boosting	Regularizált döntési fa
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	1990–1997	1984
Megalkotó	Schapire, R. E.; Freund, Y.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Típus	Sequential ensemble (iterative reweighting)	Supervised learning (regularized tree)
Alapmű	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.	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
Alternatív nevek	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Kapcsolódó	6	6
Összefoglaló	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.	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.