How to find the classification accuracy of Random Forest?

37 views (last 30 days)
MA-Winlab
MA-Winlab on 5 May 2019
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;

Answers (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.
Regards,
Zenin

Community Treasure Hunt

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

Start Hunting!

Translated by