Comparer des méthodes

	Arbre de décision auto-supervisé	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2015–present	2001
Auteur d'origine	Multiple authors (active research area, 2010s–2020s)	Breiman, L.
Type	Self-supervised ensemble/single tree model	Ensemble (bagging of decision trees)
Source fondatrice	Self-supervised learning. Wikipedia.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	SSL decision tree, self-supervised tree classifier, pseudo-label decision tree, unsupervised-guided decision tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	5	4
Résumé	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.	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.