Comparar métodos

	CatBoost Semi-supervisionado	CatBoost
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2018 (CatBoost); semi-supervised learning framework predates 2006	2018
Autor original	Prokhorenkova et al. (CatBoost); semi-supervised paradigm from Chapelle et al.	Prokhorenkova, L. et al. (Yandex)
Tipo	Semi-supervised ensemble (gradient boosting)	Gradient boosting on decision trees
Fonte seminal	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.
Outros nomes	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
Relacionados	5	5
Resumo	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.