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All model validations were conducted using the full validation dataset, including the records with missing data.
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The models were validated using the full cross validation technique, in order to determine the optimal number of latent variables and to detect the outlier samples.
Both the PCA and PLS models were validated using the full cross-validation method.
After this step, we ran OptimizePSAM using the full data on the PSAMs passed validation steps.
Due to our relatively small data set, we used 10-fold cross-validation (cv) for model development and validation, with the benefit of still using the full data set to fit the final model.
26 We retained parameter coefficients from each model (using the full prediction sample data) and applied these to the corresponding population at-risk in the validation sample dataset.
Using the full NTP database, the prediction accuracy of Progol was estimated to be 63% (+/- 3%) using 5-fold cross validation.
Using the full stacked set of descriptors (thus without the need of any specific musical descriptor) we achieved 96.19% of accuracy in cross-validation.
On run 1, we use the full set of corpora alongside a RT validation.
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