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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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Principais autores: Mehdi Imani, Ali Beikmohammadi, Hamid Reza Arabnia
פורמט: Artigo
שפה:Inglês
יצא לאור: MDPI AG 2025-02-01
סדרה:Technologies
נושאים:
customer churn prediction
different imbalance rates
XGBoost
random forest
upsampling techniques
גישה מקוונת:https://www.mdpi.com/2227-7080/13/3/88
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