Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Полу-контролируемый XGBoost× | XGBoost× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2016–2018 | 2016 |
| Автор метода≠ | Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors | Chen, T. & Guestrin, C. |
| Тип≠ | Ensemble (semi-supervised gradient boosting) | Ensemble (gradient-boosted 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 ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Другие названия≠ | SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost | XGBoost, extreme gradient boosting, scalable tree boosting |
| 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. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateНабор данных ↗ |
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