Prediction-domain adaptive cross-validation is proposed as a flexible alternative to fixed random or spatial methods for reliably estimating accuracy in environmental maps.
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Model evaluation in supervised learning should be treated as a context-dependent, decision-oriented process aligned with operational objectives rather than relying on a small set of aggregate metrics.
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Moving beyond spatial and random cross-validation in environmental modelling: a call for prediction-domain adaptive evaluation
Prediction-domain adaptive cross-validation is proposed as a flexible alternative to fixed random or spatial methods for reliably estimating accuracy in environmental maps.
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Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
Model evaluation in supervised learning should be treated as a context-dependent, decision-oriented process aligned with operational objectives rather than relying on a small set of aggregate metrics.