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Further, we have tested the method with 4 fold, 5 fold and 10 fold cross validation procedures.
To help determine whether the choice of procedure for cross validation unduly influences the present results, the PSBC method was used to compare the mean and standard deviations resulting from various kinds of cross validation procedures across the ANN, GLM and SVM techniques, Table 9.
In general, lower fold (2-fold, 3-fold) cross validation procedures tend to provide lower estimates of the R and higher estimates of the MSE due to their relatively smaller sizes of training sets when compared to the higher fold (10-fold, 20-fold) partitions.
The model performance was evaluated with cross validation procedures on 6 groups.
Figure 1 provides the 2 × 2 contingency table demonstrating the results of the leave-one-out cross validation procedures, and the associated performance calculations.
Previous neuroimaging studies have used k-fold cross validation procedures to provide unbiased estimates of generalisation in terms of feature selection, model comparison, or classification accuracy.
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We assessed the validity of the clinical model in a cross validation procedure.
The leave-one-out cross validation procedure showed an 88.23% of correct classification.
This demonstrated the effectiveness of cross validation procedure.
Additionally, a four-fold cross validation procedure was run.
The potential predictive variables were selected using a conventional stepwise method with a cross validation procedure.
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