Linganisha mbinu

	Bagging Ensemble	AdaBoost	Uimarishaji (Boosting Ensemble)	Upigaji Kura wa Wengi	Msitu Nasibu
Nyanja	Ujifunzaji wa Ensemble	Ujifunzaji wa Mashine	Ujifunzaji wa Ensemble	Ujifunzaji wa Ensemble	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning	Machine learning	Machine learning	Machine learning
Mwaka wa asili	1996	1997	1990	1996	2001
Mwanzilishi	Leo Breiman	Freund, Y. & Schapire, R.E.	Robert Schapire	Leo Breiman	Breiman, L.
Aina	parallel ensemble	Ensemble (sequential boosting of weak learners)	sequential ensemble	voting aggregation	Ensemble (bagging of decision trees)
Chanzo asilia	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.	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Majina mbadala	bootstrap aggregating	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	adaptive boosting, sequential ensemble	hard voting	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Zinazohusiana	4	5	4	5	4
Muhtasari	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.	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.	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.	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.	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.