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Calibration equations were developed to adjust FAO estimates to approximate national dietary surveys validated by using randomly split data sets.
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These were further strengthened by using randomly selected participants.
In order to facilitate a detailed experiment, a total of 48 mice subjects were used and randomly split into 3 different groups.
The 154 prototype samples identified by SigClust were randomly split into 10 groups.
To assess the optimal Ki-67 LI cut-off in different molecular classes and to avoid data overfitting, each molecular class was randomly split into two subsets by using SPSS random sampling; one third of cases were used as a training set, and the remaining two thirds used as a validation (test) set.
In each run, we use a different seed to randomly split the data and do 5-fold cross-validation.
Training and testing machine learning classifiers In this study, we used 10-fold cross validation, which randomly split each class into ten equal subsets.
Bootstrapping is implemented in Matlab using the built-in function 'randperm' to randomly split the dataset into training and testing data.
To build a predictive model, we used bootstrapping, whereby the cell lines were randomly split into training and testing sets.
The data set was randomly split into an exploratory part used to establish the hypotheses, and a confirmatory part.
The specimens were randomly split into two groups (n=6), imaged using microCT and tested under axial loading.
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