Сравнение методов

	Ансамбль деревьев решений	Дерево решений
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1996–2000	1984
Автор метода	Breiman, L.; Dietterich, T. G.	Breiman, Friedman, Olshen & Stone
Тип	Ensemble (multiple decision trees combined)	Recursive partitioning (if-then rules)
Основополагающий источник	Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Другие названия	decision tree ensemble, ensemble of decision trees, combined decision trees, multiple classifier system (decision trees)	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Связанные	6	5
Сводка	Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification and regression tasks.	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.