Linganisha mbinu

	Stacked Generalization	Bagging Ensemble	Uimarishaji (Boosting Ensemble)
Nyanja	Ujifunzaji wa Ensemble	Ujifunzaji wa Ensemble	Ujifunzaji wa Ensemble
Familia	Machine learning	Machine learning	Machine learning
Mwaka wa asili	1992	1996	1990
Mwanzilishi	David Wolpert	Leo Breiman	Robert Schapire
Aina	meta-learning aggregation	parallel ensemble	sequential ensemble
Chanzo asilia	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.	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	stacking, meta-learning	bootstrap aggregating	adaptive boosting, sequential ensemble
Zinazohusiana	3	4	4
Muhtasari	Stacked generalization, or stacking, is a two-level ensemble method where base-level classifiers are trained on the original data, and a meta-learner is trained on the predictions of the base classifiers. The meta-learner learns how to best combine base predictions rather than using fixed aggregation rules. Introduced by David Wolpert in 1992, stacking achieves state-of-the-art performance by automatically learning the optimal weighting and interaction patterns among base models.	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.