Compara mètodes

	Gradient Boosting Robust	Random Forest	Impuls de gradient regularitzat
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	2001	2001	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
Autor original	Friedman, J. H. (with Huber loss from Huber, P. J.)	Breiman, L.	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
Tipus	Ensemble (boosted trees with robust loss)	Ensemble (bagging of decision trees)	Regularized ensemble (additive tree model)
Font seminal	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.
Àlies	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
Relacionats	6	4	6
Resum	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.