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Machine learningMachine learning

Bayesiansk XGBoost

Bayesiansk XGBoost kombinerer den forudsigelseskraft, som Extreme Gradient Boosting tilbyder, med Bayesiansk optimering til finjustering af hyperparametre. I stedet for gitter- eller tilfældig søgning styrer en sandsynlighedsbaseret surrogatmodel søgningen efter optimale indlæringshastigheds-, trædybde- og regulariseringsparametre, hvilket opnår næsten optimal ydeevne med langt færre evalueringer end udtømmende søgemetoder.

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Kilder

  1. 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. DOI: 10.1145/2939672.2939785
  2. Snoek, J., Larochelle, H. & Adams, R. P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25, 2951–2959. link

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ScholarGate. (2026, June 3). Bayesian-Optimized Extreme Gradient Boosting. ScholarGate. https://scholargate.app/da/machine-learning/bayesian-xgboost

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ScholarGateBayesian XGBoost (Bayesian-Optimized Extreme Gradient Boosting). Hentet 2026-06-15 fra https://scholargate.app/da/machine-learning/bayesian-xgboost · Datasæt: https://doi.org/10.5281/zenodo.20539026