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The evaluation of each algorithm was performed using 10-fold cross-validation, where the training data is split in k equal parts (folds), and for each kth iteration, the classification algorithm is trained with k−1-folds and tested with the remaining one [33].
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The PPMI data was split in half, maintaining the same HC to PD ratio in both subgroups.
A fully validated procedure is suggested where data are split in training and test sets, ten times by default, capturing any variability or inconsistency in the data.
Data was split in a training set (2341 molecules, Additional file 2) and a test set (298 molecules, Additional file 3).
The resolution of the final reconstructions was estimated based on a Fourier shell correlation (FSC) criterion of 0.5 using the EOTEST command within EMAN, which measured the degree of correlation when the data was split in half.
In both setups the data was split in ten parts.
Data were split in 10 subsets of approximately equal size.
In this way, the data is split into two or more sub-samples in order to train, test and validate the ANN models more effectively.
In CV, the data is split into k subsets t times.
It is designed to detect hospital acquired infections even in a situation where only a restricted amount of clinical data is available (the data is split up in different information systems).
Because the Newton method is very efficient, it is usually enough for J < 15 to achieve a tolerance 10 −6) for θ(J ), Suppose data are split into U parts in terms of observations and each part contains the same variables.
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