How to create a confusion matrix using the output of crossval() function in Matlab SVM classifier?

Question

I have to measure the performance of SVM classifier in Matlab. Confusion matrix must be used as the performance measure. However, in the examples in Matlab, only loss value can be calculated. I could find no info about how to create a confusion matrix from the result of crossval() function.

SVMModel = fitcsvm(X,Y,'Standardize',true,'KernelFunction','RBF',...
    'KernelScale','auto');
CVSVMModel = crossval(SVMModel);
FirstModel = CVSVMModel.Trained{1};

Answer 1

Example Matlab code to do this.

% Example 3
% Compute the confusion matrix using stratified 10-fold cross validation:
% https://www.mathworks.com/help/stats/crossval.html

load('fisheriris');
% Class label
y = species;
% Measurments to classify with
X = meas;

% Class order
order = unique(y); % Order of the group labels

cp = cvpartition(y,'k',10); % Stratified cross-validation

f = @(xtr,ytr,xte,yte)confusionmat(yte,classify(xte,xtr,ytr),'order',order);

cfMat = crossval(f,X,y,'partition',cp);

cfMat = reshape(sum(cfMat),3,3)
% cfMat =
%     50     0     0
%      0    48     2
%      0     1    49
% cfMat is the summation of 10 confusion matrices from 10 test sets.`