Compara mètodes

	XGBoost semisupervisat	Gradient Boosting	Propagació d'etiquetes	Random Forest	XGBoost
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning	Machine learning	Machine learning
Any d'origen	2016–2018	2001	2002	2001	2016
Autor original	Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors	Friedman, J. H.	Zhu, X. & Ghahramani, Z.	Breiman, L.	Chen, T. & Guestrin, C.
Tipus	Ensemble (semi-supervised gradient boosting)	Ensemble (sequential boosting of decision trees)	Graph-based semi-supervised classification	Ensemble (bagging of decision trees)	Ensemble (gradient-boosted decision trees)
Font seminal	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.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Àlies	SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionats	4	5	3	4	5
Resum	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.	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.