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Therefore, we split the datasets into two age classes: first-year (1 year) and "adult" (> 1 year).
Briefly, a sequence length threshold of 200 bp was considered for all libraries and used to split the datasets in according to the length.
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Split the dataset into training and test datasets for evaluating the predicted performance of the model.
Then we randomly split the dataset into 10 subsets for 10-fold cross-validation.
1. Split the dataset into training and test datasets for evaluating the predicted performance of the model.
We split the dataset into a training set (80%% of the records) and a validation set (20%% of the records).
We split the dataset into two groups: group A, from May 1996 to December 2004; group B, from January 2005 to April 2009.
To find descriptive clusters for each vaccination outcome, we split the dataset into subsets according to the outcome class.
We split the dataset in two equal parts, one for parameterizing the model and another for testing that model.
Bootstrapping is implemented in Matlab using the built-in function 'randperm' to randomly split the dataset into training and testing data.
We recall that DAC 's training has split the dataset in N partitions and runs a CAP-growth over each partition, thus generating an ensemble of N models.
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CEO of Professional Science Editing for Scientists @ prosciediting.com