MATLAB: What is the meaning of loss() and predict() in the case of random forest

random forest

I'm training a random forest classifier with the following command:
rusTree = fitensemble(trainX,trainY,'RUSBoost',1000,t,'LearnRate',0.1,'nprint',100);
I'm then curious about how well the model works, and ran the following commands:
L = loss(rusTree,testX,testY,'mode','cumulative');
[label,score] = predict(rusTree,testX);
With label and the testY, I evaluated the amount of True positives and False positives in my data. Since L is the classification error, I expected that my evaluation and L would have some connection. However, they seem to be irrelevant. What exactly am I getting out of predict() and loss()?
Thank you!
June.

Best Answer

From your question I understand that you are trying to fit a random forest classifier on your training data. I assume that ' trainY' contains categorical label since it is a classification task.
The answer to your question is in two folds. In your code
L = loss(rusTree,testX,testY,'mode','cumulative');
L will be having cumulative loss. This vector indicates the loss of the model when you add trees to your ensemble one by one cumulatively.This is very different from your evaluation of false positive and negatives from prediction label,since predict function uses all the trees at a time.You can plot the loss to see the trend
 figure;
 plot(loss(bag,Xtest,Ytest,'mode','cumulative'));
 xlabel('Number of trees');
 ylabel('Test classification error');
This trend will give you the idea of how your loss is varying as the number of trees grow.
MATLAB invokes different functions depending on the function signature(Function overloading).Function signature is determined by number of inputs, types of inputs,number of outputs.' predict' function has multiple definitions.In your code
[label,score] = predict(rusTree,testX);
there is a chance that predict function corresponds to predicting image category( doc_link ) getting invoked. In that case 'score' would be having negated average binary loss per class. Please be careful in your function signature.
I would recommend you to use following code to do your evaluation.
L = loss(rusTree,testX,testY);
[label,score,node,cnum] = predict(rusTree,testX);
You can refer to this link for more details. I hope this will answer your question.
Regards,
Darshan Bhat
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