Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Полу-контролируемый XGBoost× | Градиентный бустинг× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2016–2018 | 2001 |
| Автор метода≠ | Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors | Friedman, J. H. |
| Тип≠ | Ensemble (semi-supervised gradient boosting) | Ensemble (sequential boosting of decision trees) |
| Основополагающий источник≠ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Другие названия | SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Связанные≠ | 4 | 5 |
| Сводка≠ | Semi-supervised XGBoost extends the XGBoost gradient boosting framework to settings where only a fraction of training examples carry labels. By iteratively generating pseudo-labels for unlabeled data and retraining on the expanded set, the method extracts signal from unlabeled observations, improving generalization when labeled data are scarce. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. |
| ScholarGateНабор данных ↗ |
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