Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Дерево решений× | Кластеризация методом k-средних× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1984 | 1967 |
| Автор метода≠ | Breiman, Friedman, Olshen & Stone | MacQueen, J. |
| Тип≠ | Recursive partitioning (if-then rules) | Partitional clustering (centroid-based) |
| Основополагающий источник≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297. link ↗ |
| Другие названия≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering |
| Связанные≠ | 5 | 3 |
| Сводка≠ | 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. | K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis. |
| ScholarGateНабор данных ↗ |
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