Hi Max,
Here's a bit more information regarding the 'memory not mapped' errors which
occur in caret.
1. The segfault only occurs when knitting a Markdown file in RStudio. When the
code is run 'normally' in R, everything's fine.
2. The error is very hard to replicate! It only occurs when the fo
OK, thanks.
I haven't reported the memory map errors because I haven't been able to
replicate them reliably: some times they occur, but some times don't, for the
same code. I'll have another try, and will report if I can get more information.
Thanks again.
On 18/11/2013, at 14:42 , Max Kuhn
Andrew,
> What I still don't quite understand is which accuracy values from train() I
> should trust: those using classProbs=T or classProbs=F?
It depends on whether you need the class probabilities and class
predictions to match (which they would if classProbs = TRUE).
Another option is to use
Hi Max,
Thanks very much for investigating and explaining that - your help and time is
much appreciated.
So as I understand it, using classProbs=F in trainControl() will give me the
same accuracy results as before. However, I was relying on the class
probabilities to return ROC/sensitivity/s
Or not!
The issue with with kernlab.
Background: SVM models do not naturally produce class probabilities. A
secondary model (via Platt) is fit to the raw model output and a
logistic function is used to translate the raw SVM output to
probability-like numbers (i.e. sum to zero, between 0 and 1). I
I'm using caret to assess classifier performance (and it's great!). However,
I've found that my results differ between R2.* and R3.* - reported accuracies
are reduced dramatically. I suspect that a code change to kernlab ksvm may be
responsible (see version 5.16-24 here:
http://cran.r-project.
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