Salīdzināt metodes

	Robust Decision Tree	Koku lēmumu pieņemšana (Decision Tree)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000s–2019	1984
Autors	Various (Chen & Nan 2019; robust statistics community)	Breiman, Friedman, Olshen & Stone
Tips	Supervised classification / regression tree	Recursive partitioning (if-then rules)
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.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Citi nosaukumi	robust tree, noise-tolerant decision tree, outlier-resistant decision tree, robust CART	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Saistītās	6	5
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 Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.