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Thus, the variability of the error estimates in the outer loop reached a minimum for moderately sized test sets.
Thus, the variability of the error estimates in the outer loop (ave.vb (PE )) decreased with larger test data set sizes owing to less variable test data.
A slight increase in the prediction error estimates might be acceptable in order to decrease the variability of the error estimates considerably.
In this simulation study, the variability of the error estimates changed considerably for test data set sizes up to n test = 7 and then changed only slightly for larger test set sizes.
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Moreover, using frequent splits also allows studying the variability of the prediction error estimates.
The variability of the prediction error estimates from the outer loop (ave.vb (PE )) quickly decreases for larger test sets.
Interestingly, the variability of PE oracle differed completely from the variability of the prediction error estimates in the outer loop.
In this simulation study the variability of the prediction error estimates derived from the outer loop was remarkably high in case of only one single test object.
According to the distribution-based method measuring the variability of the standard error of measurement (SEM), we compared the change in clinical outcomes (VAS and AOFAS score).
n test = 1 in the outer loop, however, is not the ideal choice since the variability of the prediction error estimate is rather high in this case which is shown in Figure 5.
However, there was also a drawback since larger training data sets imply smaller test sets: in case of small test data set sizes, the variability of the prediction error estimates increased considerably.
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