Krahasoni metodat

	LightGBM me Mësim Aktiv	Përmbledhja me Gradient (Gradient Boosting)	Pylli i Rastësishëm
Fusha	Mësimi i makinës	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning	Machine learning
Viti i origjinës	2017–present	2001	2001
Krijuesi	Settles, B. (active learning); Ke, G. et al. (LightGBM)	Friedman, J. H.	Breiman, L.
Lloji	Hybrid (active learning query strategy + gradient boosting classifier)	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)
Burimi themelues	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Emërtime të tjera	AL-LightGBM, Active LightGBM, LightGBM active learning, AL-LGBM	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Të lidhura	5	5	4
Përmbledhja	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.	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.