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Though hardware and software improvements have facilitated and spread the use of prediction models, methodologies for evaluation of the model performance have not kept pace.
Exchange fluxes that are critical for model performance have been identified for metabolites of mouse tumor necrosis factor, histidine, valine and lysine, as well as biomass.
However, observations used as "truth" to assess model performance have themselves properties unique to the data collection protocols, siting and spatial density of deployment.
The problem addressed in this contribution is that of transferability – making projections into situations that are statistically independent from the training data, and such measures of model performance have been shown to be overly optimistic [38].
Alternatively, bootstrapping procedures that make full use of the data and give nearly unbiased estimates of future model performance have been advocated [ 5, 7– 9].
Similar(55)
Model performance has been evaluated by comparing the global modeling outcomes of numerous aerosol modules.
The model performance has resulted to be quite promising.
Furthermore, the model performance has been compared to empirical indoor models.
Thus, influence of the simulation period on model performance has been examined for key meteorological parameters.
The model performance has been validated using an experimental data of a 500 W commercial PEM fuel cell stack.
In the present study, a detailed investigation on air dust particles effect on photovoltaic (PV) model performance has been carried out.
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