As could cross validation in a neural network NarX?
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I would like to know how to raise the cross-validation for NarX neural network, in this way, not destroy correlations. I have a set of 1136 data and 1136 data input targets.
thank you very much
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Shashank Prasanna
le 14 Fév 2013
You may want to use DIVIDEBLOCK instead of the default dividerand. DIVIDEBLOCK will maintain correlation since it doesn't shuffle the data but takes block of it for crossvalidation:
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