विधियों की तुलना करें

	स्व-पर्यवेक्षित निर्णय वृक्ष	ग्रेडिएंट बूस्टिंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2015–present	2001
प्रवर्तक	Multiple authors (active research area, 2010s–2020s)	Friedman, J. H.
प्रकार	Self-supervised ensemble/single tree model	Ensemble (sequential boosting of decision trees)
मौलिक स्रोत	Self-supervised learning. Wikipedia.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
उपनाम	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
संबंधित	5	5
सारांश	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.