The micro-average directly combines the entries in the confusion matrix constructed from different labels prior to calculating the predictive performance, and the macro-average calculates the areas under the curves for each label independently and then takes the average.
The area under the receiver operating curve (AUC) was calculated to identify the predictive performance of SBF.
The root mean square error (RMSE) and percentage root mean square error (% RMSE) are also calculated to indicate the predictive performance [25, 28].
Cross-validation scores were calculated to help evaluate the predictive performance of features selected by SVM-RFE-CV.
Receiver operating characteristic (ROC) curve and area under curve (AUC) were calculated to help evaluating the predictive performance of the SVM.
To evaluate the predictive performance, we calculated the prediction mean-squared error for regression and the error rate for classification.
The receiver operating characteristic (ROC) curves were calculated in order to evaluate the predictive performance of each index.
The sensitivity, specificity, positive predictive value, negative predictive value and the receiver operating characteristic (ROC) curves were calculated in order to evaluate the predictive performance of each index.
To assess the predictive performance, we performed two analyses.
The predicted AUC0 12 (AUCpred), calculated with the 24 different LSS, were validated by determining the predictive performance as described by Sheiner and Beal [ 24].
In order to test the predictive performance of a structural classifier, we calculated the electrostatic potential resulting from p2 sequences as proposed by Dybowski et al. [ 9].
