Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Puolivalvottu CatBoost× | Semi-supervised XGBoost× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2018 (CatBoost); semi-supervised learning framework predates 2006 | 2016–2018 |
| Kehittäjä≠ | Prokhorenkova et al. (CatBoost); semi-supervised paradigm from Chapelle et al. | Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors |
| Tyyppi≠ | Semi-supervised ensemble (gradient boosting) | Ensemble (semi-supervised gradient boosting) |
| Alkuperäislähde≠ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. In Advances in Neural Information Processing Systems (NeurIPS), 31. link ↗ | 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 ↗ |
| Rinnakkaisnimet | SSL CatBoost, semi-supervised gradient boosting with CatBoost, CatBoost with unlabeled data, pseudo-label CatBoost | SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | Semi-supervised CatBoost applies CatBoost's ordered gradient boosting framework to settings where only a fraction of training instances carry labels, leveraging unlabeled data through pseudo-labeling or consistency-based strategies to improve model accuracy beyond what labeled data alone would allow. | 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. |
| ScholarGateAineisto ↗ |
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