These values represent accuracy, completeness, and the harmonic mean of precision and recall, respectively.
Ac, Sn, Sp, Pe and MCC represent Accuracy, Sensitivity, specificity, Precision and Matthews's Correlation Coefficient, respectively.
Bland-Altman methodology was used to compare two methods of measurement, where the mean difference between the two methods is the bias and represents accuracy, while the 95% limits of agreement (LOA) is the bias ± 1.96 SD and represents precision.
The measures were evaluated using Cronbach's alpha, Pearson correlations with each other, and conditional probability indices to represent accuracy in predicting scores from the Achenbach System for Empirically Based Assessment (ASEBA; Achenbach & Rescorla, 2000).
In these plots, we show the relative increase as: mathrm{Gain}=frac{Aleft(mathrm{DIAMoND}right)- Aleft(mathrm{Parallel}right)}{Aleft(mathrm{Parallel}right)}, where A can represent accuracy, sensitivity, specificity, or precision.
The area under the ROC curve (AUC) provides an overall measure of the model's classification accuracy, with a value of one representing perfect accuracy.
The area under the ROC curve (AUC) provides an overall measure of classification accuracy of the model, with the value of one representing perfect accuracy.
The performance is evaluated by root mean squared error representing the accuracy, rank error representing the suitability of metamodels when coupled with evolutionary optimization algorithms, training time representing the efficiency and variation of root mean squared error representing the robustness.
The predictability, representing the accuracy of prediction, obtained from cross-validation in training population has been evaluated in maize, wheat and barley (VanRaden 2008; Crossa et al. 2017).
The percentage recovery was close to 100% which indicated no interference of any other compound and representing the accuracy of the method.
