Hi Max,
Thanks for the note. In your last paragraph, did you mean "in
createDataPartition"? I'm a little vague about what returnTrain option
does.
Bonnie
Quoting Max Kuhn <mxk...@gmail.com>:
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
splits).
createFolds is exclusively for k-fold CV. Their usage is simular when
you use the returnTrain = TRUE option in createFolds.
Max
On Sun, Oct 2, 2011 at 4:00 PM, Steve Lianoglou
<mailinglist.honey...@gmail.com> wrote:
Hi,
On Sun, Oct 2, 2011 at 3:54 PM, <bby2...@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
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