In each validation trial, validators saw one of the 504 validation stimuli.
It is difficult to validate all of these potential biomarkers because each validation requires it own specific and sensitive test, which together is rate-limiting.
Multivariate analysis revealed the classifier to be the strongest predictor of outcome in each validation series.
For each validation a receiver operating characteristic plot (ROC plot) and truth table were generated.
As a consequence each validation level involves the simultaneous fortification with complex mixtures containing different concentrations of each analyte.
Each validation resource for example, contains information about the dataset and the model, so the underlying procedures can be invoked.
Detailed results on the running time for every property, and trace are plotted, for each validation environment, in Figure 7.
Table 3 lists the MAPEs (in %) of each validation year under the 6-fold cross validation setting.
Local PLSR uses only calibration samples that are spectral neighbors of each validation sample, thus builds one model per validation sample.
Despite the well-known stability and reproducibility problems of SERS, the validation was performed using two operators and five batches of nanoparticles, one for each validation day.
To test and compare each validation solution, we randomly generated traces of XML events with abstract names p0, p1, etc., each containing a random integer value.
