[R] difference between createPartition and createfold functions

Max Kuhn mxkuhn at gmail.com
Mon Oct 3 02:45:40 CEST 2011

Basically, createDataPartition is used when you need to make one or
more simple two-way splits of your data. For example, if you want to
make a training and test set and keep your classes balanced, this is
what you could use. It can also make multiple splits of this kind (or
leave-group-out CV aka Monte Carlos CV aka repeated training test

createFolds is exclusively for k-fold CV. Their usage is simular when
you use the returnTrain = TRUE option in createFolds.


On Sun, Oct 2, 2011 at 4:00 PM, Steve Lianoglou
<mailinglist.honeypot at gmail.com> wrote:
> Hi,
> On Sun, Oct 2, 2011 at 3:54 PM,  <bby2103 at columbia.edu> wrote:
>> Hi Steve,
>> Thanks for the note. I did try the example and the result didn't make sense
>> to me. For splitting a vector, what you describe is a big difference btw
>> them. For splitting a dataframe, I now wonder if these 2 functions are the
>> wrong choices. They seem to split the columns, at least in the few things I
>> tried.
> Sorry, I'm a bit confused now as to what you are after.
> You don't pass in a data.frame into any of the
> createFolds/DataPartition functions from the caret package.
> You pass in a *vector* of labels, and these functions tells you which
> indices into the vector to use as examples to hold out (or keep
> (depending on the value you pass in for the `returnTrain` argument))
> between each fold/partition of your learning scenario (eg. cross
> validation with createFolds).
> You would then use these indices to keep (remove) the rows of a
> data.frame, if that is how you are storing your examples.
> Does that make sense?
> -steve
> --
> Steve Lianoglou
> Graduate Student: Computational Systems Biology
>  | Memorial Sloan-Kettering Cancer Center
>  | Weill Medical College of Cornell University
> Contact Info: http://cbio.mskcc.org/~lianos/contact
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.



More information about the R-help mailing list