Comparer des méthodes

	Forêt aléatoire auto-supervisée	Arbre de décision
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2012–2022	1984
Auteur d'origine	Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage)	Breiman, Friedman, Olshen & Stone
Type	Semi-supervised ensemble (self-supervised pretext task + RF)	Recursive partitioning (if-then rules)
Source fondatrice	Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Alias	SSL-RF, self-supervised RF, self-supervised ensemble forest, unsupervised random forest with self-labeling	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Apparentées	6	5
Résumé	Self-supervised Random Forest (SSL-RF) extends the classic random forest to settings where labeled examples are scarce. The forest is first trained using automatically generated pseudo-labels derived from a self-supervised pretext task — such as predicting data transformations or masked features — and then refined on whatever true labels are available, marrying the label-efficiency of self-supervised learning with the robustness of ensemble trees.	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.