Сравнение методов

	Дерево решений с активным обучением	Полуавтоматическое дерево решений
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1984–2010	2000s
Автор метода	Settles, B. (active learning framework); Breiman et al. (decision tree base)	Various (Levin & Shapiro; Zhu & Goldberg lineage)
Тип	Active learning with decision tree base learner	Semi-supervised classifier / regressor
Основополагающий источник	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.	Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning.
Другие названия	AL-DT, active decision tree, query-based decision tree learning, uncertainty-sampling decision tree	SSDT, semi-supervised tree induction, self-training decision tree, label-propagation tree
Связанные	5	4
Сводка	Active learning with a decision tree combines the interpretable structure of a CART-style tree with a query strategy that selects the most informative unlabeled instances for human annotation. The model iteratively requests labels only for examples it is most uncertain about, minimising labeling cost while maximising classification accuracy on tabular data.	A Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve better accuracy than a fully supervised tree trained on the labeled subset alone, which is especially valuable when labeling is expensive or time-consuming.