Porovnať metódy

	Stacking	Náhodný les
Odbor	Strojové učenie	Strojové učenie
Rodina	Machine learning	Machine learning
Rok vzniku	1992	2001
Tvorca	Wolpert, D.H.	Breiman, L.
Typ	Ensemble (heterogeneous meta-learning)	Ensemble (bagging of decision trees)
Pôvodný zdroj	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Ďalšie názvy	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Príbuzné	5	4
Zhrnutie	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.	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.