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Data are split into training and validation groups using one of several methods, such as splitting based on birth year or relationship.
Data are split into blocks and stored randomly across the data nodes.
The data are split into two sets: a training data set and a validating data set.
Random: Data are split randomly into a training and test set of defined sizes.
When the data are split into twenty clusters, the errors do not decrease with distance from source but remain constant.
During training, historical time series data are split into small time windows and prediction targets are set for each window.
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Instead, responsibility for data is split between all the dozens of different offices that handle the agency's different reporting regimes.
Parent data were split into lower and higher BAP groups.
The data were split randomly into train and test subsets.
All data is split into two non-overlapping sets: the training set and the test set.
Figure 1 shows how the data is split and loaded onto the targeted GPU. Figure 1 An example of how data is split and loaded onto the GPU.
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