Compara mètodes

	Impuls regularitzat	XGBoost
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2001–2016	2016
Autor original	Friedman, J. H.; extended by Chen & Guestrin	Chen, T. & Guestrin, C.
Tipus	Regularized ensemble (boosting with shrinkage/penalty)	Ensemble (gradient-boosted decision trees)
Font seminal	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Àlies	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionats	5	5
Resum	Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks.	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.