So sánh phương pháp

	Rừng Ngẫu nhiên Tự giám sát	Cây Quyết định
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2012–2022	1984
Người khởi xướng	Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage)	Breiman, Friedman, Olshen & Stone
Loại	Semi-supervised ensemble (self-supervised pretext task + RF)	Recursive partitioning (if-then rules)
Công trình gốc	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.
Tên gọi khác	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
Liên quan	6	5
Tóm tắt	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.