Salīdzināt metodes

	Robust XGBoost	Robust Random Forest
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2016 (XGBoost); robust loss concept from 1964	2000s–2010s
Autors	Chen, T. & Guestrin, C. (XGBoost); Huber, P. J. (robust loss)	Various (extensions of Breiman 2001 Random Forest)
Tips	Ensemble (gradient boosting with robust objective)	Robust Ensemble (noise-tolerant bagging of decision trees)
Pirmavots	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.	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.
Citi nosaukumi	XGBoost with Huber loss, outlier-robust gradient boosting, robust GBDT, XGBoost robust regression	RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest
Saistītās	6	6
Kopsavilkums	Robust XGBoost combines the scalable gradient boosting framework of XGBoost with robust loss functions — primarily the Huber loss or its variants — to produce a gradient boosted tree ensemble that resists the distorting influence of outliers. By replacing the squared-error objective with a loss that down-weights large residuals, the model delivers reliable predictions on continuous targets even when training data contain extreme values or label noise.	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.