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The choice of these two locations facilitated the comparison of model performances in different geographical areas.
In this paper, we proposed two methods (APMI and CPMI) that identify the suitable model for a query chemical compound based on the model performances in its Pareto neighbourhood.
All ROC plots and area-under-the-curve (AUC) metrics used the ROCR package [59] in R. Averaging of model performances in the ROC plots was done with vertical averaging of performance at a given false-positive rate, and error bars give the standard deviation.
We then compared the model performances in fitting to the data and in forecasting future observations.
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Dots represent the model performance in the low (red), moderate (green) and high (blue) performer classes.
Sylvester et al. [ 33] performed bootstrap resampling of all the IPD available and then examined model performance in these samples, leading to a bias-corrected C-statistic.
The model performance in different scales can be evaluated better.
Validation of clinical prediction models traditionally refers to the assessment of model performance in new patients.
Several statistical measures were applied to quantify the model performance in and above the breathing zone.
The model performance in terms of network latency, energy consumption, and scalability is analyzed through simulation.
An example of model performance in a long-term cropping systems trial is provided.
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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