Ton: Does preprocessing (scaling, removing constant variables, etc.) "by hand" of the whole data set *before* splitting resolve things?
You will need the same variable structure in the training and the test set anyway; scaling is just the first code part that fails on your data... g, -d ----- Hi all - _ I am running into a problem with the SVM() method when applying it to data sets that have descriptors with zero variance. Here is the sequence of events: 1. I split my data set with 512 descriptors in a training and test set 2. I build an SVM model for the training set. Out of 512 descriptors, 500 have zero variance which I discard before calling the SVM method 3. For the test set, 8 descriptors have zero variance, which I discard too 4. predict.svm() then fails, because it tries to scale using two vectors of different size (500 and 504) Is there a way to get around this? -- Dr. David Meyer Department of Information Systems Vienna University of Economics and Business Administration Augasse 2-6, A-1090 Wien, Austria, Europe Fax: +43-1-313 36x746 Tel: +43-1-313 36x4393 HP: http://wi.wu-wien.ac.at/~meyer/ ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html