방법 비교

	자기 지도 랜덤 포레스트	XGBoost
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2012–2022	2016
창시자	Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage)	Chen, T. & Guestrin, C.
유형	Semi-supervised ensemble (self-supervised pretext task + RF)	Ensemble (gradient-boosted decision trees)
원전	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.
별칭	SSL-RF, self-supervised RF, self-supervised ensemble forest, unsupervised random forest with self-labeling	XGBoost, extreme gradient boosting, scalable tree boosting
관련	6	5
요약	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.