Linganisha mbinu

	AdaBoost	Bagging Ensemble	Uimarishaji (Boosting Ensemble)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Ensemble	Ujifunzaji wa Ensemble
Familia	Machine learning	Machine learning	Machine learning
Mwaka wa asili	1997	1996	1990
Mwanzilishi	Freund, Y. & Schapire, R.E.	Leo Breiman	Robert Schapire
Aina	Ensemble (sequential boosting of weak learners)	parallel ensemble	sequential ensemble
Chanzo asilia	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. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.
Majina mbadala	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	bootstrap aggregating	adaptive boosting, sequential ensemble
Zinazohusiana	5	4	4
Muhtasari	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.	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.	Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by Schapire (1990) and refined in AdaBoost (Freund & Schapire, 1997), boosting converts weak learners (barely better than random) into strong learners through sequential reweighting.