পদ্ধতির তুলনা করুন

	অ্যাক্টিভ লার্নিং লাইটজিবিএম (Active Learning LightGBM)	XGBoost
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017–present	2016
প্রবর্তক	Settles, B. (active learning); Ke, G. et al. (LightGBM)	Chen, T. & Guestrin, C.
ধরন	Hybrid (active learning query strategy + gradient boosting classifier)	Ensemble (gradient-boosted decision trees)
মৌলিক উৎস	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.
অপর নাম	AL-LightGBM, Active LightGBM, LightGBM active learning, AL-LGBM	XGBoost, extreme gradient boosting, scalable tree boosting
সম্পর্কিত	5	5
সারসংক্ষেপ	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.