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This evaluation with dispersion modelling has shown that previous LUR model validation methods may have been optimistic in their assessment of the model's predictive performance at residential locations.

One of the most employed model validation methods in systems and synthetic biology is model checking.

Thus, bootstrap resampling approaches may become the model validation methods of choice.

Another common approach is to utilize internal model validation methods, like cross-validation to try to achieve a generalizable NN model.

In either case, improved model parameterization and a strong emphasis on model validation methods and uncertainty analyses need to be employed [ 14, 16, 21, 63].

Classical model validation methods (e.g. k-fold cross-validation or Receiving Operator Characteristic (ROC) curves) are suitable to validate probabilistic but not discrete results [ 17], which are represented by a single point in the ROC space.

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Input prediction model, validation method.

The results of these two validation problems show the feasibility and effectiveness of the proposed model validation method.

Then, not only can the possibility of failure be estimated but also the credibility of the possibility of failure based on the proposed model validation method.

Open image in new window Fig. 5 Model validation method #2: confidence interval testing Open image in new window Fig. 6 Linear regression: all three high density organizations (empirical and mode.

In order to assess the predictive ability of the developed models, two validation methods, leave one out cross validation and external validation, were conducted.

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