Usporedite metode

	Robusno pojačanje gradijenta	Slučajna šuma	Regularizirani gradijentni boosting
Područje	Strojno učenje	Strojno učenje	Strojno učenje
Obitelj	Machine learning	Machine learning	Machine learning
Godina nastanka	2001	2001	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
Tvorac	Friedman, J. H. (with Huber loss from Huber, P. J.)	Breiman, L.	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
Vrsta	Ensemble (boosted trees with robust loss)	Ensemble (bagging of decision trees)	Regularized ensemble (additive tree model)
Temeljni izvor	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.	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.
Drugi nazivi	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
Srodne	6	4	6
Sažetak	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.	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.	Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.