Starting Off on the Wrong Foot: Pitfalls in Data Preparation
When working with real-world insurance data, practitioners often encounter challenges during the data preparation stage that can undermine the statistical validity and reliability of downstream modeling. This study illustrates that conventional data preparation procedures such as random train-test partitioning, often yield unreliable and unstable results when confronted with highly imbalanced insurance loss data. To mitigate these limitations, we propose a novel data preparation framework leveraging two recent statistical advancements: support points for representative data splitting to ensure distributional consistency across partitions, and the Chatterjee correlation coefficient for ini...
| Authors | Jiayi Guo, Panyi Dong, Zhiyu Quan |
|---|---|
| Primary category | stat.ML |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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