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.
