手法を比較

	ロバスト勾配ブースティング	ブースティング
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2001	1990–1997
提唱者	Friedman, J. H. (with Huber loss from Huber, P. J.)	Schapire, R. E.; Freund, Y.
種類	Ensemble (boosted trees with robust loss)	Sequential ensemble (iterative reweighting)
原典	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.
別名	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
関連	6	6
概要	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.