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By doing so, N balanced datasets are created, on which a prediction model is trained to distinguish minority and majority classes.
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Different datasets were created for training, testing, and cross validation.
Three separate datasets were created for analyses of HAs.
Twenty imputed datasets were created for each year/gender combination.
Twenty imputed datasets were created using imputations based on variables predictive of missing status including gender, age, alcohol, cannabis & drugs use, bullying others and measures of depression, school connectedness & peer attachment.
The distance matrices on these datasets were created using DNADIST PHYLIPP version 3.68) and the sequences were subsequently clustered using NEIGHBOUR PHYLIPP version 3.68).
Training and validation datasets were created by splitting each full dataset based on year.
These datasets were created by the University of Washington ENCODE group14,29.
Two distinct datasets were created.
First of all, artificial test datasets were created.
21 datasets were created from the screening data.
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