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Table 1 shows the obtained performance measures of nine employed classification algorithms for differentiation between RBP chains and Ctrl chains, in a LOOCV analysis.
The unbiased methylation age acquired using LOOCV in the WHZH panel showed a similar prediction accuracy with a correlation coefficient of 0.95 and a RMSE of 4.05 (Figure 1).
In order to estimate the prediction performance of our newly proposed model PADLMP, the leave-one-out cross validation (LOOCV) procedure was adopted based on the positive samples of disease-LMPair associations.
We use LOOCV to evaluate how well the classifiers detect LASA cases, as the results of LOOCV are reproducible [41].
For this reason, LOOCV has the lowest bias since the training set contains the whole dataset except one datum.
Each curve represents the average matching results of one method obtained from the LOOCV test.
The parameters of LS-SVM models ([gamma] and [sigma]) were optimized by minimizing the prediction errors of leave-one-out cross validation (LOOCV) using the Simplex optimization.
For CMFMDA, we can obtain its test results with three different ways: 5-fold CV, Local LOOCV, and global LOOCV.
In the following experiments, a leave-one-out-cross-validation (LOOCV) strategy is conducted to evaluate every HAR method.
Additionally, the leave-one-out cross- validation (LOOCV) method was used to evaluate the accuracy of these parameters in determining mentor's satisfaction.
In this study, Leave-one-out cross-validation (LOOCV) was used, and the discrimination percentage was calculated based on the number of cases that were correctly predicted by the discriminant function.