So sánh phương pháp

	Rừng Ngẫu nhiên Tự giám sát	XGBoost
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2012–2022	2016
Người khởi xướng	Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage)	Chen, T. & Guestrin, C.
Loại	Semi-supervised ensemble (self-supervised pretext task + RF)	Ensemble (gradient-boosted decision trees)
Công trình gốc	Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Tên gọi khác	SSL-RF, self-supervised RF, self-supervised ensemble forest, unsupervised random forest with self-labeling	XGBoost, extreme gradient boosting, scalable tree boosting
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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.