Porovnat metody

	Samoučené rozhodovací stromy	Rozhodovací strom
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2015–present	1984
Tvůrce	Multiple authors (active research area, 2010s–2020s)	Breiman, Friedman, Olshen & Stone
Typ	Self-supervised ensemble/single tree model	Recursive partitioning (if-then rules)
Původní zdroj	Self-supervised learning. Wikipedia.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Další názvy	SSL decision tree, self-supervised tree classifier, pseudo-label decision tree, unsupervised-guided decision tree	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Příbuzné	5	5
Shrnutí	Self-supervised Decision Tree learning combines the interpretability of classical decision trees with the ability to exploit large quantities of unlabeled data through self-supervised pretext tasks. The model learns useful feature representations or node-split criteria from unlabeled samples before refining predictions on a small labeled set, bridging the gap between fully supervised trees and purely unsupervised clustering.	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.