Linganisha mbinu

	Uimarishaji (Boosting Ensemble)	AdaBoost	Bagging Ensemble	Upigaji Kura wa Wengi
Nyanja	Ujifunzaji wa Ensemble	Ujifunzaji wa Mashine	Ujifunzaji wa Ensemble	Ujifunzaji wa Ensemble
Familia	Machine learning	Machine learning	Machine learning	Machine learning
Mwaka wa asili	1990	1997	1996	1996
Mwanzilishi	Robert Schapire	Freund, Y. & Schapire, R.E.	Leo Breiman	Leo Breiman
Aina	sequential ensemble	Ensemble (sequential boosting of weak learners)	parallel ensemble	voting aggregation
Chanzo asilia	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.	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.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Majina mbadala	adaptive boosting, sequential ensemble	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	bootstrap aggregating	hard voting
Zinazohusiana	4	5	4	5
Muhtasari	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.	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.	Majority voting is an ensemble method that combines predictions from multiple base classifiers by selecting the class that receives the most votes. Each base classifier casts one vote for a predicted class, and the final prediction is the class with the majority (plurality). This approach was formalized by Leo Breiman and colleagues in the 1990s as a simple yet effective way to improve classification accuracy.