Azzera filtri
Azzera filtri

How to train an SVM classifier and calculate performance

9 visualizzazioni (ultimi 30 giorni)
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".

Categorie

Scopri di più su Statistics and Machine Learning Toolbox in Help Center e File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by