পদ্ধতির তুলনা করুন

	ব্যাচিং এনসেম্বল	অ্যাডাবুস্ট
ক্ষেত্র	এনসেম্বল শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1996	1997
প্রবর্তক	Leo Breiman	Freund, Y. & Schapire, R.E.
ধরন	parallel ensemble	Ensemble (sequential boosting of weak learners)
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.
অপর নাম	bootstrap aggregating	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma
সম্পর্কিত	4	5
সারসংক্ষেপ	Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.