[R] to determine the variable importance in svm

[Max Kuhn]

> The caret package has answers to all your questions.

>> 1) How to obtain a variable (attribute) importance using
>> e1071:SVM (or other
>> svm methods)?

I haven't implemented a model-specific method for variables importance
for SVM models. I know of one package (svmpath) that will return the
regression coefficients (e.g. the \beta values of x'\beta) for two
class models. There are probably other methods for non-linear kernels,
but I haven't coded anything (any volunteers?).

When there is no variable importance method implemented for
classification models, caret calculates an ROC curve for each
predictor and returns the AUC. For 3+ classes, it returns the maximum
AUC on the one-vs-all ROC curves.

Note also that caret uses ksvm in kernlab for no other reason that it
has a bunch of available kernels and similar methods (rvm, etc)

>> 2) how to validate the results of svm?

If you use caret, you can look at:

  http://user2010.org/slides/Kuhn.pdf
  http://www.jstatsoft.org/v28/i05

and the four package vignettes.

Max