Models trained with forward feature selection guided by spatial cross-validation drop harmful geolocation predictors and yield more reliable spatial maps than standard approaches.
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Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial prediction
Models trained with forward feature selection guided by spatial cross-validation drop harmful geolocation predictors and yield more reliable spatial maps than standard approaches.