Compara mètodes

	Arbre de decisió semi-supervisat	Random Forest
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2000s	2001
Autor original	Various (Levin & Shapiro; Zhu & Goldberg lineage)	Breiman, L.
Tipus	Semi-supervised classifier / regressor	Ensemble (bagging of decision trees)
Font 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. (2001). Random Forests. Machine Learning, 45, 5–32.
Àlies	SSDT, semi-supervised tree induction, self-training decision tree, label-propagation tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relacionats	4	4
Resum	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.	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.