Salīdzināt metodes

	Robust Decision Tree	Random Forest
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000s–2019	2001
Autors	Various (Chen & Nan 2019; robust statistics community)	Breiman, L.
Tips	Supervised classification / regression tree	Ensemble (bagging of decision trees)
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. (2001). Random Forests. Machine Learning, 45, 5–32.
Citi nosaukumi	robust tree, noise-tolerant decision tree, outlier-resistant decision tree, robust CART	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Saistītās	6	4
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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.