পদ্ধতির তুলনা করুন

	অনলাইন ব্যাগিং	গ্রেডিয়েন্ট বুস্টিং	অনলাইন বুস্টিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2001	2001	2001
প্রবর্তক	Oza, N. C. & Russell, S.	Friedman, J. H.	Oza, N. C. & Russell, S.
ধরন	Online ensemble (streaming bagging)	Ensemble (sequential boosting of decision trees)	Online ensemble (incremental boosting)
মৌলিক উৎস	Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 105–112.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.
অপর নাম	incremental bagging, streaming bagging, online bootstrap aggregating, OzaBag	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting
সম্পর্কিত	4	5	6
সারসংক্ষেপ	Online Bagging is a streaming ensemble method introduced by Oza and Russell in 2001 that adapts the classical bootstrap aggregating (Bagging) framework to the online learning setting. Instead of resampling a fixed dataset, each incoming instance is fed to every base learner a Poisson(1)-distributed number of times, faithfully approximating bootstrap sampling as the stream evolves. The result is a robust, incrementally updated ensemble that can handle concept drift and continuous data arrival without storing the entire dataset.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.