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Results of initial analytical studies, including validation of the model using test data available in the literature, are presented.
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However, because of the differences between modelling algorithms, methodologies and patterns of relative suitability, comparing models using test statistics and selecting a 'best' model are not recommended.
For example, in the SZII dataset, in MAE and MSE terms, the forecasting results of the series models using test dataset improved by 2.41% and 2.51% compared to those of the parallel hybrid models.
Linear regression models used test scores as dependent variables: verbal general ability, non-verbal general ability, reading comprehension and mathematics.
It splits the content of the input file into training and test sets, creates a simplified solubility model, and tests the model using the test set.
FE models were validated using test data that were generated elsewhere for structural health monitoring.
C-statistics were compared between the three models (pragmatic model, test model, and ideal model) using nonparametric tests (39).
The validity of the FZC was tested on the final model using sensitivity tests.
In addition to LOO CV of training samples, we further tested our model using 2 different test set samples.
A model specification test was carried out for the OLS regression model using theRamsey RESET test.
The models were checked using tests of non-proportional odds for all variables.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com