> 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 ______________________________________________ R-help@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.
