Comparar métodos

	Boosting Regularizado	Gradient Boosting Robusto
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2001–2016	2001
Autor original	Friedman, J. H.; extended by Chen & Guestrin	Friedman, J. H. (with Huber loss from Huber, P. J.)
Tipo	Regularized ensemble (boosting with shrinkage/penalty)	Ensemble (boosted trees with robust loss)
Fonte seminal	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.
Outros nomes	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees
Relacionados	5	6
Resumo	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.	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.