So sánh phương pháp

	Cây quyết định tự giám sát	Gradient Boosting
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2015–present	2001
Người khởi xướng	Multiple authors (active research area, 2010s–2020s)	Friedman, J. H.
Loại	Self-supervised ensemble/single tree model	Ensemble (sequential boosting of decision trees)
Công trình gốc	Self-supervised learning. Wikipedia.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Tên gọi khác	SSL decision tree, self-supervised tree classifier, pseudo-label decision tree, unsupervised-guided decision tree	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Liên quan	5	5
Tóm tắt	Self-supervised Decision Tree learning combines the interpretability of classical decision trees with the ability to exploit large quantities of unlabeled data through self-supervised pretext tasks. The model learns useful feature representations or node-split criteria from unlabeled samples before refining predictions on a small labeled set, bridging the gap between fully supervised trees and purely unsupervised clustering.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.