পদ্ধতির তুলনা করুন

	Bagging (Bootstrap Aggregating)	স্ট্যাকিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1996	1992
প্রবর্তক	Breiman, L.	Wolpert, D.H.
ধরন	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (heterogeneous meta-learning)
মৌলিক উৎস	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
অপর নাম	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
সম্পর্কিত	5	5
সারসংক্ষেপ	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.