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For model calibration and uncertainty analysis, the k-fold cross validation approach was used.
To measure exact classification performance, cross validation approach was adopted in our experiments.
"Cross validation" approach was considered to split the available data in this study.
To evaluate the performance of the prediction model, we adopted a five-fold cross validation approach.
To test the performance of each classifier, we employed a k-fold cross validation approach (Refaeilzadeh et al., 2009).
A three components model was found optimal using a cross validation approach provided by SIMCA-P 12 [34].
Similar(50)
Twenty-five different calibration models were validated by cross-validation approach with 25 left out standards.
The results were validated using a standard leave-one-out cross-validation approach.
The same cross-validation approach and statistical testing was used.
LDA classification was performed separately for each participant and time point using a leave-one-out cross-validation approach.
Our bootstrap approach is robust to overfitting errors, and therefore does not require a cross-validation approach.
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