Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daļēji uzraudzīts XGBoost× | Gradient Boosting× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2016–2018 | 2001 |
| Autors≠ | Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors | Friedman, J. H. |
| Tips≠ | Ensemble (semi-supervised gradient boosting) | Ensemble (sequential boosting of decision trees) |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | 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. |
| ScholarGateDatu kopa ↗ |
|
|