Comparer des méthodes

	Arbre de décision en apprentissage actif	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1984–2010	2001
Auteur d'origine	Settles, B. (active learning framework); Breiman et al. (decision tree base)	Breiman, L.
Type	Active learning with decision tree base learner	Ensemble (bagging of decision trees)
Source fondatrice	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	AL-DT, active decision tree, query-based decision tree learning, uncertainty-sampling decision tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
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.	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.