Сравнение на методи

	Случайна гора	Дърво на решенията
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2001	1984
Създател	Breiman, L.	Breiman, Friedman, Olshen & Stone
Тип	Ensemble (bagging of decision trees)	Recursive partitioning (if-then rules)
Основополагащ източник	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Други названия	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Свързани	4	5
Резюме	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.	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.