পদ্ধতির তুলনা করুন

	Bagging (Bootstrap Aggregating)	বুস্টিং	ফেডারেটেড লার্নিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	গোপনীয়তা
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	1996	1990–1997	2017
প্রবর্তক	Breiman, L.	Schapire, R. E.; Freund, Y.	McMahan et al.
ধরন	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Sequential ensemble (iterative reweighting)	Distributed privacy-preserving machine learning
মৌলিক উৎস	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.
অপর নাম	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
সম্পর্কিত	5	6	3
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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model.