विधियों की तुलना करें

	एक्टिव लर्निंग लाइटजीबीएम	XGBoost
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2017–present	2016
प्रवर्तक	Settles, B. (active learning); Ke, G. et al. (LightGBM)	Chen, T. & Guestrin, C.
प्रकार	Hybrid (active learning query strategy + gradient boosting classifier)	Ensemble (gradient-boosted decision trees)
मौलिक स्रोत	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.
उपनाम	AL-LightGBM, Active LightGBM, LightGBM active learning, AL-LGBM	XGBoost, extreme gradient boosting, scalable tree boosting
संबंधित	5	5
सारांश	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.