Порівняння методів

	Дерево рішень	Ієрархічна кластеризація
Галузь	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	1984	1963
Автор методу	Breiman, Friedman, Olshen & Stone	Ward, J. H.
Тип	Recursive partitioning (if-then rules)	Unsupervised clustering (agglomerative)
Основоположне джерело	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Інші назви	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Пов'язані	5	4
Підсумок	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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.