পদ্ধতির তুলনা করুন

	অ্যাক্টিভ লার্নিং স্ট্যাকিং এনসেম্বল	স্ট্যাকিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1992–2012	1992
প্রবর্তক	Wolpert, D. H. (stacking); Settles, B. (active learning survey)	Wolpert, D.H.
ধরন	Hybrid (active learning + stacked ensemble)	Ensemble (heterogeneous meta-learning)
মৌলিক উৎস	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
অপর নাম	AL-stacking, query-by-committee stacking, active stacked generalization, stacking with active query	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
সম্পর্কিত	5	5
সারসংক্ষেপ	Active Learning Stacking Ensemble combines an active learning query loop with stacked generalization: a pool of unlabeled data is available, and the model iteratively selects the most informative instances for human labeling, using those labels to train and refine a stacking ensemble of multiple base learners topped by a meta-learner. This approach reduces annotation cost while maximizing the predictive power of the ensemble.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.