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| 준지도형 CatBoost× | 그래디언트 부스팅× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2018 (CatBoost); semi-supervised learning framework predates 2006 | 2001 |
| 창시자≠ | Prokhorenkova et al. (CatBoost); semi-supervised paradigm from Chapelle et al. | Friedman, J. H. |
| 유형≠ | Semi-supervised ensemble (gradient boosting) | Ensemble (sequential boosting of decision trees) |
| 원전≠ | 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 ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| 별칭 | SSL CatBoost, semi-supervised gradient boosting with CatBoost, CatBoost with unlabeled data, pseudo-label CatBoost | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| 관련 | 5 | 5 |
| 요약≠ | 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. | 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. |
| ScholarGate데이터셋 ↗ |
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