Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles 事件

Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles arXiv:2606.07582v1 Announce Type: cross Abstract: Customer churn prediction is essential across data-driven industries such as insurance, digital banking, eCommerce, and subscription platforms, where retaining existing customers is typically more cost-effective than acquiring new ones. Predicting churn on structured datasets remains challenging due to class imbalance, nonlinear feature interactions, and het