I'm new to supervised classification. Here's my case:

I want to classify subjects in 3 classes: healthy, sick and intermediate. I've been asked to use SVM to do the classification. I know how it works, that you have to have a training set, a testing set; cross validation etc... but i'm confused on which classes should I built the model?

So, of course, I thought of doing 3 separate SVM classification (completely independent): SVM healthy/sick, SVM healthy/intermediate, SVM sick/intermediate. Then study accuracy, sensitivity, specificity, AUC for the 3 separated SVM classification...

Is it acceptable or it doesn't work like that?

PS: i'm using the caret package

I'm new to supervised classification. Here's my case:

I want to classify subjects in 3 classes: healthy, sick and intermediate. I've been asked to use SVM to do the classification. I know how it works, that you have to have a training set, a testing set; cross validation etc... but i'm confused on which classes should I built the model with?

So, of course, I thought of doing 3 separate SVM classification (completely independent): SVM healthy/sick, SVM healthy/intermediate, SVM sick/intermediate. Then study accuracy, sensitivity, specificity, AUC for the 3 separated SVM classification...

Is it acceptable or it doesn't work like that?

PS: i'm using the caret package