Salīdzināt metodes

	CatBoost daļēji uzraudzīta apmācība	CatBoost
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2018 (CatBoost); semi-supervised learning framework predates 2006	2018
Autors	Prokhorenkova et al. (CatBoost); semi-supervised paradigm from Chapelle et al.	Prokhorenkova, L. et al. (Yandex)
Tips	Semi-supervised ensemble (gradient boosting)	Gradient boosting on decision trees
Pirmavots	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.	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.
Citi nosaukumi	SSL CatBoost, semi-supervised gradient boosting with CatBoost, CatBoost with unlabeled data, pseudo-label CatBoost	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma
Saistītās	5	5
Kopsavilkums	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.	CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data.