BAYESIAN OPTIMIZATION FOR TUNING HYPERPARAMETRS OF MACHINE LEARNING MODELS: A PERFORMANCE ANALYSIS IN XGBOOST

The performance of machine learning models depends on the selection and tuning of hyperparameters. As a widely used gradient boosting method, XGBoost relies on optimal hyperparameter configurations to balance model complexity, prevent overfitting, and improve generalization. Especially in high-dime...

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Autors principals:	Микола ЗЛОБІН, Володимир БАЗИЛЕВИЧ
Format:	Artigo
Idioma:	Inglês
Publicat:	Khmelnytskyi National University 2025-03-01
Col·lecció:	Компютерні системи та інформаційні технології
Matèries:	XGBoost Bayesian optimization hyperparameter tuning machine learning tree-structured parzen estimator
Accés en línia:	https://csitjournal.khmnu.edu.ua/index.php/csit/article/view/372
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