Methoden vergleichen

	Semi-supervidiertes CatBoost	Gradient Boosting
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2018 (CatBoost); semi-supervised learning framework predates 2006	2001
Urheber	Prokhorenkova et al. (CatBoost); semi-supervised paradigm from Chapelle et al.	Friedman, J. H.
Typ	Semi-supervised ensemble (gradient boosting)	Ensemble (sequential boosting of decision trees)
Wegweisende Quelle	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Aliasnamen	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
Verwandt	5	5
Zusammenfassung	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.