Comparar métodos

	Árvore de Decisão Regularizada	Árvore de Decisão
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1984	1984
Autor original	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Breiman, Friedman, Olshen & Stone
Tipo	Supervised learning (regularized tree)	Recursive partitioning (if-then rules)
Fonte seminal	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Outros nomes	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Relacionados	6	5
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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.