Bandingkan kaedah

	Active Learning LightGBM	Pembelajaran Aktif	Peningkatan Cerun
Bidang	Pembelajaran Mesin	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	2017–present	2009	2001
Pengasas	Settles, B. (active learning); Ke, G. et al. (LightGBM)	Burr Settles	Friedman, J. H.
Jenis	Hybrid (active learning query strategy + gradient boosting classifier)	Interactive supervised learning framework	Ensemble (sequential boosting of decision trees)
Sumber perintis	Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alias	AL-LightGBM, Active LightGBM, LightGBM active learning, AL-LGBM	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Berkaitan	5	2	5
Ringkasan	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.	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.	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.