Comparer des méthodes

	Arbre de décision en apprentissage actif	Arbre de décision semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1984–2010	2000s
Auteur d'origine	Settles, B. (active learning framework); Breiman et al. (decision tree base)	Various (Levin & Shapiro; Zhu & Goldberg lineage)
Type	Active learning with decision tree base learner	Semi-supervised classifier / regressor
Source fondatrice	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.
Alias	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
Apparentées	5	4
Résumé	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.