Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels

This study examines the efficacy of Random Forest and XGBoost classifiers in conjunction with three upsampling techniques—SMOTE, ADASYN, and Gaussian noise upsampling (GNUS)—across datasets with varying class imbalance levels, ranging from moderate to extreme (15% to 1% churn rate). Employing metric...

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Detalhes bibliográficos

Principais autores:	Mehdi Imani, Ali Beikmohammadi, Hamid Reza Arabnia
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2025-02-01
coleção:	Technologies
Assuntos:	customer churn prediction different imbalance rates XGBoost random forest upsampling techniques
Acesso em linha:	https://www.mdpi.com/2227-7080/13/3/88
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