R help - Nov 2006 - How to find AUC in SVM (kernlab package)

Dear all,
I was wondering if someone can help me. I am learning SVM for 
classification in my research with kernlab package. I want to know about 
classification performance using Area Under Curve (AUC). I know ROCR 
package can do this job but I found all example in ROCR package have 
include prediction, for example, ROCR.hiv {ROCR}. My problem is how to 
produce prediction in SVM and to find AUC.

Here is a simple example:

library(MASS)
library(kernlab)
library(ROCR)

pimamodel <- ksvm(type ~
.,data=Pima.tr,type="C-svc",C=10,prob.model=TRUE)
pimamodel
fitted(pimamodel)

pima.pred <- predict(pimamodel, Pima.te[,-8], type="probabilities")
pima.pred

# try to find AUC
#predid.no  <- prediction(pima.pred[,1], Pima.te[,8])
#predid.yes <- prediction(pima.pred[,2], Pima.te[,8])
predid <- prediction(pima.pred, Pima.te[,8])
perfid <- performance(predid,"tpr","fpr")
perfid.auc <- performance(predid,"auc")
perfid.auc

Thank you very much for your help.

Best wishes, Muhammad Subianto

Hi
  you need predict.ksvm() function.
  
  for more information see The kernlab package here:
   http://lib.stat.cmu.edu/R/CRAN/doc/packages/kernlab.pdf
  
  cheers,
  Amir
  

Muhammad Subianto <msubianto@gmail.com> wrote:  Dear all,
I was wondering if someone can help me. I am learning SVM for 
classification in my research with kernlab package. I want to know about 
classification performance using Area Under Curve (AUC). I know ROCR 
package can do this job but I found all example in ROCR package have 
include prediction, for example, ROCR.hiv {ROCR}. My problem is how to 
produce prediction in SVM and to find AUC.

Here is a simple example:

library(MASS)
library(kernlab)
library(ROCR)

pimamodel <- ksvm(type ~
.,data=Pima.tr,type="C-svc",C=10,prob.model=TRUE)
pimamodel
fitted(pimamodel)

pima.pred <- predict(pimamodel, Pima.te[,-8], type="probabilities")
pima.pred

# try to find AUC
#predid.no  <- prediction(pima.pred[,1], Pima.te[,8])
#predid.yes <- prediction(pima.pred[,2], Pima.te[,8])
predid <- prediction(pima.pred, Pima.te[,8])
perfid <- performance(predid,"tpr","fpr")
perfid.auc <- performance(predid,"auc")
perfid.auc

Thank you very much for your help.

Best wishes, Muhammad Subianto

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