Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Бэггинг (Бутстрэп-агрегирование)× | Чрезвычайно случайные деревья× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1996 | 2006 |
| Автор метода≠ | Breiman, L. | Geurts, P.; Ernst, D.; Wehenkel, L. |
| Тип≠ | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) | Ensemble (extremely randomized decision trees) |
| Основополагающий источник≠ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ | Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. DOI ↗ |
| Другие названия≠ | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor | Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET |
| Связанные | 5 | 5 |
| Сводка≠ | Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner. | 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. |
| ScholarGateНабор данных ↗ |
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