So sánh phương pháp

	LightGBM Học Chủ Động	Gradient Boosting
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2017–present	2001
Người khởi xướng	Settles, B. (active learning); Ke, G. et al. (LightGBM)	Friedman, J. H.
Loại	Hybrid (active learning query strategy + gradient boosting classifier)	Ensemble (sequential boosting of decision trees)
Công trình gốc	Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Tên gọi khác	AL-LightGBM, Active LightGBM, LightGBM active learning, AL-LGBM	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Liên quan	5	5
Tóm tắt	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.