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
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
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
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
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'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:
6 matches
Mail list logo
