เปรียบเทียบวิธี

	Bagging (Bootstrap Aggregating)	AdaBoost	Random Forest
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning	Machine learning
ปีกำเนิด	1996	1997	2001
ผู้ริเริ่ม	Breiman, L.	Freund, Y. & Schapire, R.E.	Breiman, L.
ประเภท	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (sequential boosting of weak learners)	Ensemble (bagging of decision trees)
แหล่งต้นตำรับ	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
ชื่อเรียกอื่น	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
ที่เกี่ยวข้อง	5	5	4
สรุป	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.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.