Sammenlign metoder

	Boosting	Reguleret beslutningstræ
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1990–1997	1984
Ophavsperson	Schapire, R. E.; Freund, Y.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Type	Sequential ensemble (iterative reweighting)	Supervised learning (regularized tree)
Oprindelig kilde	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
Aliasser	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Relaterede	6	6
Resumé	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.