arXiv cs.AI· Ismail Hossain Polas·· 10 小时前AI 评分26
TabPFN-3.5 与调优 GBDT 在电信流失预测上的对比：利润与校准感知评估
Tabular Foundation Models vs Tuned Gradient Boosting for Telecom Churn: A Profit- and Calibration-Aware Evaluation
AI 导读
研究在五个公开电信流失数据集、10 次种子划分上重新评估 TabPFN-2.5、TabPFN-3.5、TabPFN-3.5-Fast 和 TabICL v2 与 Optuna 调优 GBDT 的表现。
正文 · 原文
Abstract:Tabular foundation models (TFMs) predict from a labelled context in a single forward pass. A recent benchmark reported that they decisively outperform tree ensembles for customer churn, but it compared them with untuned gradient-boosted trees (GBDTs) and scored only ranking metrics, while a retention campaign is judged in money. We re-evaluate TabPFN-2.5, TabPFN-3.5, TabPFN-3.5-Fast and TabICL v2 against Optuna-tuned GBDTs on five public telecom churn datasets over 10 seeded splits. We separate three questions: whether a model ranks churners well, whether that ranking earns more expected maximum profit (EMP), and whether its probabilities can drive a threshold policy. With a split-aware test and Holm correction over 20 pre-declared comparisons, TabPFN-3.5 earns significantly more EMP than the best tuned GBDT on Cell2Cell (1.9%) and Iranian (7.6%), and these two gains also hold against each of the three tuned GBDTs separately. On Orange it is ahead in all 10 splits (14.3%), but the gain is not significant after correction (Holm p = 0.155). The splits resample the same datasets, so these results do not stand for independent operators. TabICL v2, the earlier benchmark's leader, beats the best tuned GBDT only on Iranian, not all three of them, and underpredicts Orange churn by 2.1 points on a 7.3% base rate. On the datasets and splits where we tested them, a substantially larger tuning budget and a duplicate-aware split leave the main conclusions unchanged. Retuning the baseline cuts the earlier headline gap from 6.1 to 4.0 PR-AUC points without closing it. The licence of the TabPFN-3.5 weights we used (License v1.0), not scoring cost, is what stands between its gains and production use.
| Comments: | 17 pages, 2 figures, 14 tables. Code, per-split predictions and reproducibility audit: this https URL (archived at this https URL) |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| ACM classes: | I.2.6; H.2.8 |
| Cite as: | arXiv:2611.12942 [cs.AI] |
| (or arXiv:2611.12942v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.12942 arXiv-issued DOI via DataCite |
Submission history
From: Ismail Hossain Polas [view email]
[v1]
Fri, 6 Nov 2026 17:15:06 UTC (49 KB)
来源：arXiv cs.AI · arxiv.org