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Robust Gradient Boosting

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.

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Robust Gradient Boosting (Gradient Boosting with Robust Loss Functions)

Rekod kaedah taksonomik · ml-model / machine-learning

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