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Model validation was carried out using a leave one out cross-validation.
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The performance of the classifier was tested using a leave-one-out cross-validation strategy.
Finally, the quality of the inferred annotations was assessed using a leave-one-out approach.
LDA classification was performed separately for each participant and time point using a leave-one-out cross-validation approach.
With the entire soil samples, GA-PLSR and PLSR models were calibrated for the four heavy metals using a leave-one-out cross-validation procedure.
Experiment was carried out using a "leave-one-out" cross-validation scheme.
CAD performance was assessed using a leave-one-out sampling scheme and Receiver Operating Characteristics analysis.
The remaining 30 epochs were used for classifier training/testing using a leave-one-out cross-validation paradigm.
Prediction accuracy was evaluated using a leave-one-out procedure which estimated the scores of each individual using a model trained with the rest of the data.
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