_:n5e6e90b5bba04fda9428c60473b84661xb7
<http://schema.org/description>
"Predictive modelling using machine learning has become very popular for\nspatial mapping of the environment. Models are often applied to make\npredictions far beyond sampling locations where new geographic locations might\nconsiderably differ from the training data in their environmental properties.\nHowever, areas in the predictor space without support of training data are\nproblematic. Since the model has no knowledge about these environments,\npredictions have to be considered uncertain.\n  Estimating the area to which a prediction model can be reliably applied is\nrequired. Here, we suggest a methodology that delineates the \"area of\napplicability\" (AOA) that we define as the area, for which the cross-validation\nerror of the model applies. We first propose a \"dissimilarity index\" (DI) that\nis based on the minimum distance to the training data in the predictor space,\nwith predictors being weighted by their respective importance in the model. The\nAOA is then derived by applying a threshold based on the DI of the training\ndata where the DI is calculated with respect to the cross-validation strategy\nused for model training. We test for the ideal threshold by using simulated\ndata and compare the prediction error within the AOA with the cross-validation\nerror of the model. We illustrate the approach using a simulated case study.\n  Our simulation study suggests a threshold on DI to define the AOA at the .95\nquantile of the DI in the training data. Using this threshold, the prediction\nerror within the AOA is comparable to the cross-validation RMSE of the model,\nwhile the cross-validation error does not apply outside the AOA. This applies\nto models being trained with randomly distributed training data, as well as\nwhen training data are clustered in space and where spatial cross-validation is\napplied.\n  We suggest to report the AOA alongside predictions, complementary to\nvalidation measures."
<https://deepai.org/profile/hanna-meyer>