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The datasets were split into training set used during learning and the test set used during evaluation.
Before applying the framework, the two used datasets were split in four subsets of data (Table 7).
Daily datasets were split into 12 h, 6 h and 3 h subsets in order to investigate the impact of data length on estimated displacement.
The modeling datasets were split into training and validation subsets.
Datasets were split to obtain the test set/training set accuracy estimate.
(B ) The datasets were split into <5 and ≥5 copies to ensure that the observed difference was not an artefact of the arbitrary two-copy cut-off used in Figure 4B.
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Last, the datasets were splitted into training set and testing set.
In this procedure, the datasets are split into training and testing sets in 10 different ways.
Each of the datasets was split into a reference and validation set (Table 1) to allow for cross validation of the accuracy of DGV.
Each of the three list-mode datasets was split into 60 noise realizations each containing the same number of list-mode events, which were spread out regularly over the entire scan time.
The scores in the original dataset were split into five population-weighted quintiles based on the national distribution.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com