Comparar métodos

	Árvore de Decisão Regularizada	Random Forest
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1984	2001
Autor original	Breiman, L., Friedman, J., Olshen, R., & Stone, C.	Breiman, L.
Tipo	Supervised learning (regularized tree)	Ensemble (bagging of decision trees)
Fonte seminal	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Outros nomes	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relacionados	6	4
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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.