Porovnat metody

	Aktivní učení	Online Learning	Random Forest
Obor	Strojové učení	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	2009	1958–2000s	2001
Tvůrce	Burr Settles	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	Breiman, L.
Typ	Interactive supervised learning framework	Learning paradigm (sequential model update)	Ensemble (bagging of decision trees)
Původní zdroj	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	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.
Další názvy	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	incremental learning, sequential learning, streaming learning, online machine learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Příbuzné	2	6	4
Shrnutí	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.	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.