পদ্ধতির তুলনা করুন

	স্ব-পর্যবেক্ষিত সিদ্ধান্ত বৃক্ষ	গ্রেডিয়েন্ট বুস্টিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2015–present	2001
প্রবর্তক	Multiple authors (active research area, 2010s–2020s)	Friedman, J. H.
ধরন	Self-supervised ensemble/single tree model	Ensemble (sequential boosting of decision trees)
মৌলিক উৎস	Self-supervised learning. Wikipedia.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
অপর নাম	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
সম্পর্কিত	5	5
সারসংক্ষেপ	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.