Comparer des méthodes

	Arbre de décision robuste	Extra Trees
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s–2019	2006
Auteur d'origine	Various (Chen & Nan 2019; robust statistics community)	Geurts, P.; Ernst, D.; Wehenkel, L.
Type	Supervised classification / regression tree	Ensemble (extremely randomized decision trees)
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.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
Alias	robust tree, noise-tolerant decision tree, outlier-resistant decision tree, robust CART	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
Apparentées	6	5
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.	Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time.