مقایسهٔ روش‌ها

	یادگیری فعال	XGBoost
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2009	2016
پدیدآور	Burr Settles	Chen, T. & Guestrin, C.
نوع	Interactive supervised learning framework	Ensemble (gradient-boosted decision trees)
منبع بنیادین	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
نام‌های دیگر	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	XGBoost, extreme gradient boosting, scalable tree boosting
مرتبط	2	5
خلاصه	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.	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.