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Old 03-07-2013, 09:58 PM
palmipede palmipede is offline
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Default cross validation and feature selection

Feature selection isn't exactly machine learning but could you please expand on the relationship between cross validation and feature extraction?

Some years ago, I took part in a genomic study that tried to identify genes that are up regulated in the presence of infection. These studies start with a few vectors (essays) with many components (genes) between 20k and 40k at the time and ask which components are meaningful features. The data was particularly noisy and after listening to the course I realize that what I did then really was a poor's man cross validation. I would do it much more cleanly now.

Do you recommend having different data sets for feature extraction, training and testing, or could feature extraction be part of cross-correlation during training?
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