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The external validation score of full-model is also calculated to compare the performance with final predictive model with selected features (SF-model).
In order to avoid estimating an over-optimistic validation score, we applied a nested (3 times 3 times 10 -fold cross-validation.
The smoothness parameters that control the trade-off between fit and smoothness, is selected by minimisation of the generalised cross validation score (GCV) [15].
The same procedure is repeated for various values of the hyperparameters, and the combination of the hyperparameters giving the classifier with the smallest validation score is retained.
To each recognized term, we associate a chemical entity in Chemical Entities of Biological Interest (ChEBI) ontology [3], and calculate a validation score based on its similarity to other terms.
The validation score is the overall classification error rate, computed by counting the error on all examples in the validation sets, thereby providing an estimate of the performance of the classifier.
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The initial set of models were, robust validation scores notwithstanding, either unable to recognize any of the structurally different actives they were challenged with, or else tended to predict any molecule outside the training set as active.
To show the results more clearly, we also get the average validation scores for each algorithm in these datasets which are shown in the last lines of the three metrics in Table 4.
The average of trSNPs validation scores was the lowest at 1.27, followed by the nsSNPs at 2.03.
Some of the truncation CVs with low validation scores (Figure S1) were found to be disease-causing mutations.
Regarding variable selection, Figure 3C shows that the correlation coefficients and the validation scores reach a plateau when longitude and latitude are included in the hidden regression model.
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