Aligning Validation with Deployment in Spatial Prediction: Target-Weighted Cross-Validation
Reliable estimation of predictive performance is essential for spatial environmental modeling, where machine-learning models are used to generate maps from unevenly distributed observations. Standard cross-validation (CV) assumes that validation data are representative of prediction conditions across the target domain. In practice, this assumption is often violated due to preferential or clustered sampling, leading to biased performance and uncertainty estimates. We introduce a deployment-oriented validation framework based on weighted CV that aligns validation tasks with the distribution of prediction tasks across a specified domain. The framework includes importance-weighted cross-valid...
| Authors | Alexander Brenning, Thomas Suesse |
|---|---|
| Primary category | cs.LG |
| Published | 31 March 2026 |
| Last updated at source | 21 May 2026 |
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