Comparar métodos

	Árbol de Decisión Semisupervisado	Árbol de Decisión
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2000s	1984
Autor original	Various (Levin & Shapiro; Zhu & Goldberg lineage)	Breiman, Friedman, Olshen & Stone
Tipo	Semi-supervised classifier / regressor	Recursive partitioning (if-then rules)
Fuente 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.
Alias	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
Resumen	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.