title: How to get ksvm to predict non-scaled values after scaled training
body: <p>When I run an SVM with <code>ksvm</code> from the <code>kernlab</code> package, all the outputs from the <code>predict</code> command on my final model are scaled.  I know this is because I initiate <code>scaled = T</code> but I also know scaling your data is preferred in SVM modeling.  How can I easily tell <code>ksvm</code> to return non-scaled predictions?  If not, is there a way to just manipulate the predicted scaled values to raw values?  Thank you, code is below:</p>
  
  <pre><code>svm1 &lt;- ksvm(Y ~ 1
              + X1
              + X2
              , data = data_nn
              , scaled=T
              , type = "eps-svr"
              , kernel="anovadot"
              , epsilon = svm1_CV2$bestTune$epsilon
              , C = svm1_CV2$bestTune$C
              , kpar = list(sigma = svm1_CV2$bestTune$sigma
                            , degree=  svm1_CV2$bestTune$degree)  
              ) 
  
  #Analyze Results
  data_nn$svm_pred &lt;- predict(svm1)
  </code></pre>