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Robust Gradient Boosting

Robust Gradient Boosting er gradient boosting trent med utligger-resistente tapsfunksjoner — oftest Huber-tapet eller kvantil- (pinball-) tapet — i stedet for kvadrert-feiltap. Foreslått i Friedmans banebrytende artikkel fra 2001, produserer denne varianten prediksjoner som er langt mindre forvrengt av ekstreme verdier eller kontaminerte etiketter, samtidig som den beholder den fulle prediktive kraften til gradient-boostede trær.

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Kilder

  1. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI: 10.1214/aos/1013203451
  2. Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI: 10.1214/aoms/1177703732

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ScholarGate. (2026, June 3). Robust Gradient Boosting (Gradient Boosting with Robust Loss Functions). ScholarGate. https://scholargate.app/no/machine-learning/robust-gradient-boosting

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ScholarGateRobust Gradient Boosting (Robust Gradient Boosting (Gradient Boosting with Robust Loss Functions)). Hentet 2026-06-15 fra https://scholargate.app/no/machine-learning/robust-gradient-boosting · Datasett: https://doi.org/10.5281/zenodo.20539026