Сравнение на методи

	Активно обучение	Градиентен бустинг	XGBoost
Област	Машинно обучение	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning	Machine learning
Година на възникване	2009	2001	2016
Създател	Burr Settles	Friedman, J. H.	Chen, T. & Guestrin, C.
Тип	Interactive supervised learning framework	Ensemble (sequential boosting of decision trees)	Ensemble (gradient-boosted decision trees)
Основополагащ източник	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Други названия	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	XGBoost, extreme gradient boosting, scalable tree boosting
Свързани	2	5	5
Резюме	Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.	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.	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.