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	Półnadzorowany las losowy	Gradient Boosting	Random Forest
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	2009	2001	2001
Twórca	Leistner, C., Saffari, A., Santner, J., & Bischof, H.	Friedman, J. H.	Breiman, L.
Typ	Semi-supervised ensemble classifier	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)
Źródło pierwotne	Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Inne nazwy	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Pokrewne	3	5	4
Podsumowanie	Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation.	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.	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.