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

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arXiv:2606. 07582v1 Announce Type: new 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.

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Authors: Joyjit Roy, Samaresh Kumar Singh, Laxmi Shaw
Released: 2026-06-09 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, text, class, under, token, line, rate, contexts. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.07582

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