পদ্ধতির তুলনা করুন

	Robust Bagging (রোবাস্ট ব্যাগিং)	Robust Boosting
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1996–2000s	1999–2001
প্রবর্তক	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Freund, Y.; Mason, L. et al.
ধরন	Ensemble (robust bootstrap aggregating)	Ensemble (robust sequential boosting)
মৌলিক উৎস	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.
অপর নাম	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting
সম্পর্কিত	6	6
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.	Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors.