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By testing the models at three different ranges of scour depths, we determined that while the MLP model was the most accurate model in the low scour depth range, the RBF model was more accurate in the higher range of scour depths.
The results were got by testing the models on benchmark dataset through jackknife validation.
Predictive models were validated by assessing R-square statistics and by testing the models in sub-samples of the study population.
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By testing the model for a range of incentives, we demonstrate its capability to inform and evaluate policy options.
We calibrate the sigmoid loss function by testing the model on a set of cross-validation examples taken out of the training set before optimizing the model.
The fidelity of the model has been evaluated by testing the model against experimental data from a braking system test bed.
The importance of taking into account the influence of the droplets on the fluctuations is demonstrated by testing the model with and without the corresponding terms.
By testing the model's sensitivity to the relevant parameter, we find that the pace of electrification will significantly contain the spiking of carbon prices because electricity can be produced from carbon-free or less carbon-intensive technologies.
This result was supported partially by testing the model constrained to a single latent factor.
The validity of the approach was then established by testing the model on 129 different VFM datasets.
Validation of the model was performed by testing the model code and checking results and this was performed by a second analyst.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com