Σύγκριση μεθόδων

	Ενισχυμένη Ενίσχυση Κλίσης (Robust Gradient Boosting)	Ενίσχυση Κλίσης (Gradient Boosting)	Τυχαίο Δάσος
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	2001	2001	2001
Δημιουργός	Friedman, J. H. (with Huber loss from Huber, P. J.)	Friedman, J. H.	Breiman, L.
Τύπος	Ensemble (boosted trees with robust loss)	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)
Θεμελιώδης πηγή	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	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.
Εναλλακτικές ονομασίες	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Συναφείς	6	5	4
Σύνοψη	Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.	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.