Сравнение методов

	Ансамблевое активное обучение	Активное обучение	Случайный лес
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	1992	2009	2001
Автор метода	Seung, H. S., Opper, M., & Sompolinsky, H.	Burr Settles	Breiman, L.
Тип	Ensemble-based active learning strategy	Interactive supervised learning framework	Ensemble (bagging of decision trees)
Основополагающий источник	Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT 1992), pp. 287–294. ACM.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	Query by Committee, QBC active learning, committee-based active learning, ensemble query strategy	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	5	2	4
Сводка	Ensemble Active Learning combines a committee of diverse models with an active learning loop to select the most informative unlabeled examples for labeling. Rooted in the Query by Committee framework introduced by Seung et al. (1992), it uses disagreement among committee members as a signal for uncertainty, reducing the number of labeled examples needed to achieve strong predictive performance.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.