How to train the classifier (using features extracted from images)?
I would like to train the Random forest classifier( which has 2 classes- pathology class(Tp) and non pathology class(Tn)). I have separate images to train & test the classifier. For feature extraction I should use HOG, GLCM, GLRLM. How do I train & test the classifier Using these extracted features?? I don't have any .mat file to train the classifier, I see most of the code uses mat file to train the classifier. So I don't have any idea to proceed this. Please help me with this.
John Michell answered .
2025-11-20
Use the fitctree fucntion to create a classification tree based on the training data:
tModel = fitctree(xTrain, yTrain);
See what you can do with tModel by looking at its methods:
methods(tModel)
The resulting tree can be visualized with the view() function:
view(tModel, 'mode', 'graph');
New observations can be classified using the predict() function:
yPredicted = predict(tModel, newX);
The TreeBagger() function uses bootstrap aggregation ("bagging") to create an ensemble of classification trees.
tModel = TreeBagger(50, xTrain, yTrain); % Create new model based on 50 trees.
This is a more robust model.