Сравнение методов

	Активное обучение с линейной регрессией	Случайный лес
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1996	2001
Автор метода	Cohn, D. A.; Ghahramani, Z.; Jordan, M. I.	Breiman, L.
Тип	Active learning / iterative supervised learning	Ensemble (bagging of decision trees)
Основополагающий источник	Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	AL-LR, active linear regression, query-based linear regression, optimal experimental design for regression	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	2	4
Сводка	Active Learning Linear Regression is an iterative machine-learning approach that couples a linear regression model with an intelligent query strategy to select the most informative unlabeled points for labeling. By focusing labeling effort where uncertainty is highest, it achieves competitive predictive accuracy with far fewer labeled examples than passive random sampling.	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.