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Here we show the possibilities to validate the added predictive value in a dataset where the training and validation data are different.
The software and validation data are available to download from https://github.com/dimostsag/gessa.
Key information such as spanning of intron-exon boundaries and availability of validation data are included in search results.
The adjusted R-square and residuals from validation data are used to represent model quality and performance, respectively.
Validation data are finally employed to confirm the fault diagnosis results of the SDA, where existing health state identification methods are used for comparison.
If the characteristics of the validation data are similar in information content and variability to those in the calibration period, we term this an "interpolation case", and expect the model errors during calibration to be similar to those in validation.
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Finally, the original CAMPHOR validation data were used to validate the new preference based model.
The model was validated as the model fit to validation data was even better than the fit to the calibration target data, exceeding our predefined criterion.
Long-term validation data is, however, still needed.
Validation data were presented for the two methods.
Validation data is required in order to identify and estimate all parameters.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com