Σύγκριση μεθόδων

	Stacking	Δέντρο Αποφάσεων
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1992	1984
Δημιουργός	Wolpert, D.H.	Breiman, Friedman, Olshen & Stone
Τύπος	Ensemble (heterogeneous meta-learning)	Recursive partitioning (if-then rules)
Θεμελιώδης πηγή	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Εναλλακτικές ονομασίες	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Συναφείς	5	5
Σύνοψη	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.