In svm(), how to connect quantitative prediction result to categorical result?

Hi Yunfei,

Hi,

I am studying using SVM functions of e1071 package to do prediction, and I found during the training data are "factor" type, then svm.predict() can predict data directly by categories; but if response variables are "numerical", the predicted value from svm will be continuous quantitative numbers, then how can I connect these quantitative numbers to categories? (for example:in an example data set, the response variables are numerical and have two categories: 0 and 1, and the predicted value are continuous quantitative numbers from 0 to 1.3, how can I know which of them represent category 0 and which represent 1?)

You have to figure out if you want the svm to do classification or regression.

If I remember correctly, a "vanilla" call to SVM will try to pick one
or the other in a "smart way" by looking at the types (and values) of
your labels (y vector).

You can be more explicit and tell the SVM what you want by specifying
a value for the `type` argument in your original `svm` call.

See ?svm for more info.

I'm not sure if I'm answering your question or not(?). If I didn't
understand what you wanted, perhaps you can rephrase your question, or
maybe explain how my answer is not what you were after ... otherwise
hopefully someone else can provide a better answer.

-steve

Steve Lianoglou
Graduate Student: Computational Systems Biology
?| Memorial Sloan-Kettering Cancer Center
?| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact