calculating accuracy and confusion matrix

I have used Classification Learner app for classifying 7 classes(happy, sad, angry, disgust, neutral, fear, surprise) I trained the model using SVM on the training set. Then used the trained model on the test set, it provided me labels as result, I want to calculate the accuracy rate and confusion matrix for the test set. Please help me to code it. I am new to MATLAB, and don't know how to code it.

[Mohd Syamizal Mohd Isa]

i have the problem like you, do you have the solution???

Answer 1

[Stephan]

Hi,

to compute the confusion matrix use:

confusionmat

command. Some examples are given in the confusionmat documentation.

If you have a Neural Network Toolbox™ license, you can plot the confusion matrix using

plotconfusion

which is described here.

For calculation of the accuracy you could calculate the error from your crossvalidated model using

crossval

command at first and then calculate the error in form using the

kfoldloss

command. This gives you the error from your test set for your model which should mean:

accuracy = 1 - kfoldloss

These steps are shown with examples here.

Best regards

Stephan