विधियों की तुलना करें

	मजबूत एक्सजीबूस्ट	ग्रेडिएंट बूस्टिंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2016 (XGBoost); robust loss concept from 1964	2001
प्रवर्तक	Chen, T. & Guestrin, C. (XGBoost); Huber, P. J. (robust loss)	Friedman, J. H.
प्रकार	Ensemble (gradient boosting with robust objective)	Ensemble (sequential boosting of decision trees)
मौलिक स्रोत	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
उपनाम	XGBoost with Huber loss, outlier-robust gradient boosting, robust GBDT, XGBoost robust regression	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
संबंधित	6	5
सारांश	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.