Salīdzināt metodes

	Robust Decision Tree	Regulārizēts lēmumu koks
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000s–2019	1984
Autors	Various (Chen & Nan 2019; robust statistics community)	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
Tips	Supervised classification / regression tree	Supervised learning (regularized tree)
Pirmavots	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.	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
Citi nosaukumi	robust tree, noise-tolerant decision tree, outlier-resistant decision tree, robust CART	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
Saistītās	6	6
Kopsavilkums	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.	A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees.