Comparar métodos

	Árvore de Decisão Semi-supervisionada	Árvore de Decisão
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2000s	1984
Autor original	Various (Levin & Shapiro; Zhu & Goldberg lineage)	Breiman, Friedman, Olshen & Stone
Tipo	Semi-supervised classifier / regressor	Recursive partitioning (if-then rules)
Fonte seminal	Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Outros nomes	SSDT, semi-supervised tree induction, self-training decision tree, label-propagation tree	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Relacionados	4	5
Resumo	A Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve better accuracy than a fully supervised tree trained on the labeled subset alone, which is especially valuable when labeling is expensive or time-consuming.	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.