পদ্ধতির তুলনা করুন

	Bagging (Bootstrap Aggregating)	অনলাইন বুস্টিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1996	2001
প্রবর্তক	Breiman, L.	Oza, N. C. & Russell, S.
ধরন	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Online ensemble (incremental boosting)
মৌলিক উৎস	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.
অপর নাম	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting
সম্পর্কিত	5	6
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.	Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments.