Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Чрезвычайно случайные деревья× | Дерево решений× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2006 | 1984 |
| Автор метода≠ | Geurts, P.; Ernst, D.; Wehenkel, L. | Breiman, Friedman, Olshen & Stone |
| Тип≠ | Ensemble (extremely randomized decision trees) | Recursive partitioning (if-then rules) |
| Основополагающий источник≠ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Другие названия≠ | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Связанные | 5 | 5 |
| Сводка≠ | Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time. | 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. |
| ScholarGateНабор данных ↗ |
|
|