Comparer des méthodes

	Arbre de décision robuste	Gradient Boosting Robuste
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s–2019	2001
Auteur d'origine	Various (Chen & Nan 2019; robust statistics community)	Friedman, J. H. (with Huber loss from Huber, P. J.)
Type	Supervised classification / regression tree	Ensemble (boosted trees with robust loss)
Source fondatrice	Chen, H., & Nan, F. (2019). Robust Decision Trees Against Adversarial Examples. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1006–1015.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Alias	robust tree, noise-tolerant decision tree, outlier-resistant decision tree, robust CART	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees
Apparentées	6	6
Résumé	A Robust Decision Tree is a decision tree variant trained with modified splitting criteria or training procedures designed to reduce sensitivity to outliers, label noise, and adversarial perturbations. Rather than minimizing standard impurity measures that are strongly affected by extreme values, robust variants use statistically robust analogues or regularization to produce splits that generalize under noisy or corrupted data conditions.	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.