Сравнение методов

	Онлайн-обучение	Случайный лес
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1958–2000s	2001
Автор метода	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	Breiman, L.
Тип	Learning paradigm (sequential model update)	Ensemble (bagging of decision trees)
Основополагающий источник	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	incremental learning, sequential learning, streaming learning, online machine learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	6	4
Сводка	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.	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.