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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Bibliografske podrobnosti

Principais autores:	Mehdi Imani, Ali Beikmohammadi, Hamid Reza Arabnia
Format:	Artigo
Jezik:	Inglês
Izdano:	MDPI AG 2025-02-01
Serija:	Technologies
Teme:	customer churn prediction different imbalance rates XGBoost random forest upsampling techniques
Online dostop:	https://www.mdpi.com/2227-7080/13/3/88
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