Salīdzināt metodes

	Sakņotā vispārināšana	Bagging Ensemble
Nozare	Ansambļu mācīšanās	Ansambļu mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1992	1996
Autors	David Wolpert	Leo Breiman
Tips	meta-learning aggregation	parallel ensemble
Pirmavots	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Citi nosaukumi	stacking, meta-learning	bootstrap aggregating
Saistītās	3	4
Kopsavilkums	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.