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How to train an SVM classifier and calculate performance

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Hi all,
I was already browsing through some similar question, but I still don't understand completely how to train an SVM classifier with matlab and afterwards calculate performance measures like AUC, Accuracy asf.
I managed to use fitcsvm to train a classifier and using leave-one-out cross-validation:
model=fitcsvm(data,groups,'Standardize',true,'ClassNames',{'group1','group2'},'Leaveout','on')
This works well, but how to calculate performance measures of my classifier after this step and plot the results?

Risposte (2)

Swarooph
Swarooph il 1 Ago 2016
You could do one of several things:
1. Resubstitution Loss calculation using resubLoss function
2. Loss calculation using loss function
  2 Commenti
holistic
holistic il 2 Ago 2016
Thank you! Perfcurve is what I want, but I'm not sure how to initialize it with the output from fitcsvm after leave-one-out cross-validation, i.e. where to find the "scores"?
Swarooph
Swarooph il 2 Ago 2016
If you look at the examples in the documentation, it seems to be using fitPosterior followed by resubPredict function.

Accedi per commentare.


Samaneh Nemati
Samaneh Nemati il 2 Dic 2019
you need to pass the output of svm classification (model) to predict function to get "label" and "scores".

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