مقایسهٔ روش‌ها

	یادگیری فعال LightGBM	XGBoost
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2017–present	2016
پدیدآور	Settles, B. (active learning); Ke, G. et al. (LightGBM)	Chen, T. & Guestrin, C.
نوع	Hybrid (active learning query strategy + gradient boosting classifier)	Ensemble (gradient-boosted decision trees)
منبع بنیادین	Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
نام‌های دیگر	AL-LightGBM, Active LightGBM, LightGBM active learning, AL-LGBM	XGBoost, extreme gradient boosting, scalable tree boosting
مرتبط	5	5
خلاصه	Active Learning LightGBM couples the query-efficient label-selection strategy of active learning with the speed and accuracy of LightGBM, a histogram-based gradient boosting framework. The model iteratively selects the most informative unlabeled instances for human annotation, retrains LightGBM on the growing labeled set, and converges to high accuracy with far fewer labeled examples than passive supervised learning.	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.