Comparar métodos

	Boosting	Árvore de Decisão Regularizada
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1990–1997	1984
Autor original	Schapire, R. E.; Freund, Y.	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Tipo	Sequential ensemble (iterative reweighting)	Supervised learning (regularized tree)
Fonte seminal	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
Outros nomes	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Relacionados	6	6
Resumo	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.