Bajezijanski XGBoost
Bajezijanski XGBoost kombinuje prediktivnu moć algoritma Extreme Gradient Boosting sa Bajezijanskom optimizacijom za podešavanje hiperparamетра. Umesto grid ili slučajne pretrage, verovatnosni model (surrogate model) vodi pretragu za optimalnom stopom učenja, dubinom stabla i parametrima regularizacije, postižući skoro optimalne performanse sa daleko manje evaluacija nego iscrpne metode pretrage.
Pročitajte celu metodu
Prijavite se besplatnim nalogom da biste pročitali ovaj odeljak.
Method map
The neighbourhood of related methods — select a node to explore.
Izvori
- 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 ↗
- 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 ↗
Kako citirati ovu stranicu
ScholarGate. (2026, June 3). Bayesian-Optimized Extreme Gradient Boosting. ScholarGate. https://scholargate.app/sr/machine-learning/bayesian-xgboost
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Градијентно појачањеMašinsko učenje↔ compare
- LightGBMMašinsko učenje↔ compare
- Slučajna šumaMašinsko učenje↔ compare
- XGBoostMašinsko učenje↔ compare
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