Cross Validation not as accurate because I have NaNs in my training data

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Elizabeth Drybrugh
Elizabeth Drybrugh 2018 年 4 月 1 日
編集済み: Elizabeth Drybrugh 2018 年 4 月 1 日
I am using N-1 cross-validation to train a vector to predict IQs based on connectivity values. Basically, my vector train_vec contains NaN vals. I wondering how I can fix this to get a better prediction. I have tried omitting but get the same result, tried filling the vectors with 'previous' or 'next'. If I remove this will affect the connectivities associated with a particular value.
IQ has a guassian distribution so is there any way to fill in the NaNs based on this?
New to matlab to still learning the coding...
Any help will be much appreciated

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