Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.29981 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917518707261440 |
|---|---|
| author | Brenning, Alexander Suesse, Thomas |
| author_facet | Brenning, Alexander Suesse, Thomas |
| contents | 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-validation (IWCV) and a calibration-based approach, Target-Weighted Cross-Validation (TWCV), which uses spatially meaningful task descriptors such as environmental covariates and prediction distance.
Simulation experiments show that conventional non-spatial and spatial CV strategies can exhibit substantial bias under realistic sampling designs, whereas weighted CV approaches substantially reduce this bias when validation tasks adequately cover the deployment-task space. A case study on mapping nitrogen dioxide (NO$_2$) concentrations across Germany demonstrates that standard CV can overestimate prediction error due to sampling bias, while weighted CV yields estimates more consistent with deployment conditions.
The framework separates validation task generation from risk estimation and provides a practical approach for improving performance assessment in spatial prediction settings where sample distributions differ from prediction domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29981 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Aligning Validation with Deployment in Spatial Prediction: Target-Weighted Cross-Validation Brenning, Alexander Suesse, Thomas Machine Learning I.2; J.2 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-validation (IWCV) and a calibration-based approach, Target-Weighted Cross-Validation (TWCV), which uses spatially meaningful task descriptors such as environmental covariates and prediction distance. Simulation experiments show that conventional non-spatial and spatial CV strategies can exhibit substantial bias under realistic sampling designs, whereas weighted CV approaches substantially reduce this bias when validation tasks adequately cover the deployment-task space. A case study on mapping nitrogen dioxide (NO$_2$) concentrations across Germany demonstrates that standard CV can overestimate prediction error due to sampling bias, while weighted CV yields estimates more consistent with deployment conditions. The framework separates validation task generation from risk estimation and provides a practical approach for improving performance assessment in spatial prediction settings where sample distributions differ from prediction domains. |
| title | Aligning Validation with Deployment in Spatial Prediction: Target-Weighted Cross-Validation |
| topic | Machine Learning I.2; J.2 |
| url | https://arxiv.org/abs/2603.29981 |