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Several methods for overall model fit, as well as fit within deciles of predicted costs, were used to test the predictive ability of the models.
We could check the predictive ability of the models by comparing the predicted values with the experimentally obtained results.
Solution provided by the software with the greatest desirability was chosen as the optimum condition and, after executing the experiment based on the suggested values for the independent factors, the real responses were compared with the predicted ones and error percentages were calculated to evaluate the predictive ability of the models.
The predictive ability of the models was computed as a Pearson correlation between predicted and observed values.
We assessed the predictive ability of the models using the Pearson correlation coefficient between the predicted GEBV and the observed phenotypic value.
Temporal changes in spatial distribution of MDR TB would have reduced the predictive ability of the models, yet we found that spatial information improved our predictions.
Cross validated predictive ability of the models was in the 4 9% range.
The predictive ability of the models was cross-validated by construction of a test set.
The predictive ability of the models was evaluated using Y-randomization test, cross-validation and external test set.
The predictive ability of the models is the only way we can fairly compare them, and by that measure all of the models are equally good.
To improve the predictive ability of the models, the development procedure encompassed: outliers elimination, optimum model rank definition, spectral range and spectral pre-treatment selection.
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