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The adequacy of the model equation for predicting the optimum response values was verified effectively by the validation data.
The adequacy of the predictive model equation for predicting the magnitude orders of S. aureus reduction was verified effectively by the validation data.
The evaluation by the validation data set was regarded objective for 12 species, only one of them being an indicator for reference conditions.
Moreover, the adequacy of the predictive model equation for predicting the level of L. monocytogenes reduction was verified effectively by the validation data.
This gives us once more access to many useful higher order functions provided by the Validation data type and its various type class implementations as we have shown when performing bulk operations.
Examining the Kaplan Meier curves for DFS function shows that the large separation between low and high GP88 groups seen in the training study is confirmed and, in fact, strengthened, by the validation data.
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Finally, the constructed model is validated by predicting the validation data and comparing these predictions with the real observations by means of ROC curves.
After the optimal parameters for model construction are known, the according model (final model) is validated by predicting the validation data and comparing these predictions with the real observations.
In the training phase, different ensembles of classifiers are adaptively generated by fitting the validation data globally with different degrees.
The training set is used to construct training models, the validation set is used to select the best model by applying the validation data to the best training models and selecting the best training model that produce the best validation result.
The Chi2 analysis of these subgroups allowed us to define specific molecular signatures for some of them whose reliability was further demonstrated by using the validation data set.
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