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This method involves multiple random splits of the training and test samples.
This predictive test was repeated for different training/split ratios and multiple random splits.
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Using other preprocessing methods and multiple random splitting of the data sets obtained the similar results.
We discovered a range of miRNA signatures (five to ten miRNAs) that predict for optimal chemoresponse with approximately 75% accuracy (averaged over multiple random training/test set splits of different sizes; Figure S3A in Additional file 11, Figure S4A in Additional file 12, and Figure S5 in Additional file 13).
A good method of generating committee members is to split the available data evenly into multiple random holdout and training data sets.
We report the mean recognition accuracy over 20 random splits.
However, this discretization often leads to large number of random variables, especially for multiple random fields.
We also report the mean recognition accuracy over 20 random splits.
The average recognition accuracies are reported for 30 repeated random splits.
In effect, in seven scales, the reductions were identical in all three random splits.
Firstly, the dataset was divided into three random splits and secondly each split was divided into training, calibration, test and validation sets.
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