Linganisha mbinu

	Mti Imara wa Uamuzi	Msitu Imara wa Misitu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2000s–2019	2000s–2010s
Mwanzilishi	Various (Chen & Nan 2019; robust statistics community)	Various (extensions of Breiman 2001 Random Forest)
Aina	Supervised classification / regression tree	Robust Ensemble (noise-tolerant bagging of decision trees)
Chanzo asilia	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.	Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18.
Majina mbadala	robust tree, noise-tolerant decision tree, outlier-resistant decision tree, robust CART	RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest
Zinazohusiana	6	6
Muhtasari	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 Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect.