Salīdzināt metodes

	Robust Random Forest	Koku lēmumu pieņemšana (Decision Tree)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000s–2010s	1984
Autors	Various (extensions of Breiman 2001 Random Forest)	Breiman, Friedman, Olshen & Stone
Tips	Robust Ensemble (noise-tolerant bagging of decision trees)	Recursive partitioning (if-then rules)
Pirmavots	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.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Citi nosaukumi	RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Saistītās	6	5
Kopsavilkums	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.	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.