Comparer des méthodes

	Apprentissage actif avec gradient boosting	Forêt Aléatoire	XGBoost
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2000s–2010s	2001	2016
Auteur d'origine	Settles, B. (active learning); Friedman, J. H. (gradient boosting); combined framework developed by the research community	Breiman, L.	Chen, T. & Guestrin, C.
Type	Active learning framework with gradient boosting base learner	Ensemble (bagging of decision trees)	Ensemble (gradient-boosted 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.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	AL-GBM, gradient boosting active learner, active gradient boosting, active learning with boosted trees	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
Apparentées	4	4	5
Résumé	Active Learning Gradient Boosting combines the powerful predictive accuracy of gradient boosted trees with an active learning loop that selects the most informative unlabeled examples for human annotation. By querying only the instances the model is most uncertain about, the method achieves high accuracy with far fewer labeled examples than passive supervised learning.	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.