Salīdzināt metodes

	Regularizēts "Stacking" ansamblis	Stacking
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1992–1996	1992
Autors	Wolpert, D. H. (stacking); Breiman, L. (regularized meta-learner formulation)	Wolpert, D.H.
Tips	Ensemble (stacked generalization with regularized meta-learner)	Ensemble (heterogeneous meta-learning)
Pirmavots	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Citi nosaukumi	regularized stacked generalization, ridge stacking, lasso meta-learner ensemble, penalized stacking	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Saistītās	6	5
Kopsavilkums	Regularized Stacking Ensemble is a two-level ensemble method in which predictions from multiple diverse base learners are combined by a regularized meta-learner — typically ridge regression, lasso, or elastic net — to suppress overfitting in the combination layer. Regularization ensures that the meta-learner assigns stable, well-calibrated weights to base model outputs rather than memorizing noise in the training fold predictions.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.