How to find the classification accuracy of Random Forest?

I am trying to use Random Forest with 10 fold cross validation. My code is shown below:

I would to find the correct rate of the classifier, but seems that classpref does not work with TreeBagger. In this case how can find the accuracy of the classifier given that I use cross validation ?

    cvFolds = crossvalind('Kfold', FeatureLabSHUFFLE, k);   %# get indices of 10-fold CV
    cp = classperf(FeatureLabSHUFFLE);
    
    for i = 1:k                                  %# for each fold
        testIdx = (cvFolds == i);                %# get indices of test instances
        trainIdx = ~testIdx;                     %# get indices training instances
        %Random Forest
        RFModel = TreeBagger(10,FeatureMTX(trainIdx,:), FeatureLabSHUFFLE(trainIdx));
        pred = predict(RFModel, FeatureMTX(testIdx,:));
       
        %# evaluate and update performance object
        cp = classperf(cp, pred, testIdx);
    end
    
    cp.CorrectRate;

Answer 1

[Zenin Easa Panthakkalakath]

Hi MA-Winlab,

Have a look at the following documentation that talks about Bootstrap Aggregation (Bagging) of Classification Trees Using TreeBagger. The example shows how to find the Classification accuract and loss.

https://www.mathworks.com/help/stats/classification-treeBagger-examples.html

Regards,

Zenin