So sánh phương pháp

	Rừng ngẫu nhiên	Stacking
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2001	1992
Người khởi xướng	Breiman, L.	Wolpert, D.H.
Loại	Ensemble (bagging of decision trees)	Ensemble (heterogeneous meta-learning)
Công trình gốc	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Tên gọi khác	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Liên quan	4	5
Tóm tắt	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.	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.