방법 비교

	준지도 학습 XGBoost	XGBoost
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2016–2018	2016
창시자	Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors	Chen, T. & Guestrin, C.
유형	Ensemble (semi-supervised gradient boosting)	Ensemble (gradient-boosted decision trees)
원전	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
별칭	SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost	XGBoost, extreme gradient boosting, scalable tree boosting
관련	4	5
요약	Semi-supervised XGBoost extends the XGBoost gradient boosting framework to settings where only a fraction of training examples carry labels. By iteratively generating pseudo-labels for unlabeled data and retraining on the expanded set, the method extracts signal from unlabeled observations, improving generalization when labeled data are scarce.	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.