विधियों की तुलना करें

	Robust Gradient Boosting	बूस्टिंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2001	1990–1997
प्रवर्तक	Friedman, J. H. (with Huber loss from Huber, P. J.)	Schapire, R. E.; Freund, Y.
प्रकार	Ensemble (boosted trees with robust loss)	Sequential ensemble (iterative reweighting)
मौलिक स्रोत	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
उपनाम	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
संबंधित	6	6
सारांश	Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.