Confronta i metodi

	XGBoost semi-supervisionato	Gradient Boosting	XGBoost
Campo	Apprendimento automatico	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	2016–2018	2001	2016
Ideatore	Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors	Friedman, J. H.	Chen, T. & Guestrin, C.
Tipo	Ensemble (semi-supervised gradient boosting)	Ensemble (sequential boosting of decision trees)	Ensemble (gradient-boosted decision trees)
Fonte seminale	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	XGBoost, extreme gradient boosting, scalable tree boosting
Correlati	4	5	5
Sintesi	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.	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.