পদ্ধতির তুলনা করুন

	Robust Bagging (রোবাস্ট ব্যাগিং)	Bagging (Bootstrap Aggregating)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1996–2000s	1996
প্রবর্তক	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Breiman, L.
ধরন	Ensemble (robust bootstrap aggregating)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
মৌলিক উৎস	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
অপর নাম	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
সম্পর্কিত	6	5
সারসংক্ষেপ	Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions.	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.