Which advanced algorithm has shown the highest accuracy in predicting retail customer churn when dealing with highly imbalanced datasets (where churners are a small minority)?

Customer churn is a critical challenge in retail science. Modern retailers use sophisticated predictive analytics and machine learning algorithms to identify at-risk customers before they leave. This poll tests your knowledge of the scientific approaches used in retail churn prediction models, which combine behavioral science, data analytics, and statistical modeling to help businesses retain their valuable customer base.

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XGBoost with SMOTE oversampling
Linear Regression with L1 regularization
K-means Clustering with euclidean distance metrics
Naive Bayes with Gaussian kernels