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In k-fold cross-validation, the training data is randomly partitioned into k equally sized subsets.
First, the monthly temperature data is randomly entered as different values of x.
Further, the data is randomly split into nonoverlapping train and test sets.
Unlike the control information, the payload (user data) is randomly generated at the transmitter and it is usually different for each user.
Within a single iteration, a subset of observed data is randomly selected to construct a candidate RMC with the parametric representation ({vec {mu }_{c}}).
Landslide data is randomly divided into a training (75%) and validation set (25%) and seed cells are generated by creating 25 m buffer zones around the head scarp of each scar.
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Data were randomly split into two sets.
First, collecting data was randomly selected from the entire population.
Then data were randomly divided into training, testing and validating sets.
For each rock type, data were randomly divided into two subsets, training and test sets.
The data were randomly divided into 31 training and 11 validation sets.
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