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The sets of experiments proposed by the ODOEs were evaluated using the validation models and the results were used to fit the statistical models.
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Applying and refitting models using the validation data set protects against model over-fit that can result from aggressive use of data mining procedures.
We use the training data sets to build the models and validate the selected models using the validation data sets.
We developed the mortality prediction models using the training set and then tested them using the validation set for external model validation.
Models were similarly ranked using the validation dataset, with the final model having an R 2 value of 0.990 and RMSE of 1.37 m (Table 3).
When a validation study shows disappointing results, researchers are often tempted to reject the initial model and to develop a new predictive model using the validation cohort data.
<img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0005444.e002.PNG" class= inline-graphic"/> Several metrics were examined to compare the performance of the various models using the validation set [9], [10], [24], [25], [26].
We evaluated the selection and prediction model using the validation accuracies in a LOOCV procedure.
The focus of the confirmatory study is the assessment of measurement properties and a test of a hypothesized structural model using the validation sample of 219 firms.
To highlight the overfitting problem, we re-estimated the regression coefficients of the combined model using the validation set.
We determined the optimum latent variable using the cross-validation model, and the coefficient of determination (R) of the regression model was 0.947.
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CEO of Professional Science Editing for Scientists @ prosciediting.com