Aligning Validation with Deployment in Spatial Prediction: Target-Weighted Cross-Validation

arXiv:2603.29981v3 Announce Type: replace Abstract: 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 tha

Source: arXiv cs.LG — read the full report at the original publisher.

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