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I'm trying to understant the benefit apported by the step of data augmentation in a classification algorithm. I have a vector of hexadecimal strings and a column vector containing the label associated with the string in the same position. As an optional step in the classification algorithm, a data augmentation process is performed by subsetting the strings in pieces and replating the associated label for the number of split performed.

What are the benefit of this process?

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Overfitting occurs when you have too few records relative to other parameters (e.g., predictors or features). I'm not familiar with your data, but it sounds like the subsetting is creating additional records.

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  • $\begingroup$ Ok so i create additional records that are mapped to the original ones with a relation 1 to n to overcome overfitting linked to the lack of observation of certain labels. I get that. I thought that was some other complete statistical explanation $\endgroup$ – tia_0 Jul 24 '16 at 18:49
  • $\begingroup$ @tia_0 "A data augmentation process is performed by subsetting the strings in pieces and replating the associated label for the number of split performed." That just sounds like adding more data, unless I've misinterpreted your statement $\endgroup$ – Ryan Zotti Jul 26 '16 at 19:08
  • $\begingroup$ Sorry, when I wrote back at you I was a little bit confused. You have already answered my question, thanks! $\endgroup$ – tia_0 Jul 26 '16 at 21:14
  • $\begingroup$ @tia_0 If I've answered it, could you mark it as the approved answer? It's the green check mark $\endgroup$ – Ryan Zotti Jul 26 '16 at 23:12

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