How to find the classification accuracy of Random Forest?
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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
RFModel = TreeBagger(10,FeatureMTX(trainIdx,:), FeatureLabSHUFFLE(trainIdx));
pred = predict(RFModel, FeatureMTX(testIdx,:));
%# evaluate and update performance object
cp = classperf(cp, pred, testIdx);
Zenin Easa Panthakkalakath on 14 May 2019
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.