Comparar métodos

	Árvore de Decisão Regularizada	Boosting
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1984	1990–1997
Autor original	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Schapire, R. E.; Freund, Y.
Tipo	Supervised learning (regularized tree)	Sequential ensemble (iterative reweighting)
Fonte seminal	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8	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.
Outros nomes	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Relacionados	6	6
Resumo	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.	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.