difference between createPartition and createfold functions
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.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.
Max