Methoden vergleichen

	Robust Boosting	Gradient Boosting	Regularized Boosting	Robustes Gradient Boosting
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning	Machine learning	Machine learning
Entstehungsjahr	1999–2001	2001	2001–2016	2001
Urheber	Freund, Y.; Mason, L. et al.	Friedman, J. H.	Friedman, J. H.; extended by Chen & Guestrin	Friedman, J. H. (with Huber loss from Huber, P. J.)
Typ	Ensemble (robust sequential boosting)	Ensemble (sequential boosting of decision trees)	Regularized ensemble (boosting with shrinkage/penalty)	Ensemble (boosted trees with robust loss)
Wegweisende Quelle	Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.	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.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Aliasnamen	noise-tolerant boosting, robust AdaBoost, boosting with robust losses, outlier-resistant boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees
Verwandt	6	5	5	6
Zusammenfassung	Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors.	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.	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.