Porovnat metody

	Stacking	Random Forest	Stroj s podpůrnými vektory (klasifikace)
Obor	Strojové učení	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	1992	2001	1995
Tvůrce	Wolpert, D.H.	Breiman, L.	Cortes, C. & Vapnik, V.
Typ	Ensemble (heterogeneous meta-learning)	Ensemble (bagging of decision trees)	Maximum-margin classifier (kernel method)
Původní zdroj	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Další názvy	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Příbuzné	5	4	5
Shrnutí	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.