Decision Tree in Matlab

Question

I saw the help in Matlab, but they have provided an example without explaining how to use the parameters in the 'classregtree' function. Any help to explain the use of 'classregtree' with its parameters will be appreciated.

Answer 1

The documentation page of the function classregtree is self-explanatory...

Lets go over some of the most common parameters of the classification tree model:

• x : data matrix, rows are instances, cols are predicting attributes
• y : column vector, class label for each instance
• categorical : specify which attributes are discrete type (as opposed to continuous)
• method : whether to produce classification or regression tree (depend on the class type)
• names : gives names to the attributes
• prune : enable/disable reduced-error pruning
• minparent/minleaf : allows to specify min number of instances in a node if it is to be further split
• nvartosample : used in random trees (consider K randomly chosen attributes at each node)
• weights : specify weighted instances
• cost : specify cost matrix (penalty of the various errors)
• splitcriterion : criterion used to select the best attribute at each split. I'm only familiar with the Gini index which is a variation of the Information Gain criterion.
• priorprob : explicitly specify prior class probabilities, instead of being calculated from the training data

A complete example to illustrate the process:

%# load data
load carsmall

%# construct predicting attributes and target class
vars = {'MPG' 'Cylinders' 'Horsepower' 'Model_Year'};
x = [MPG Cylinders Horsepower Model_Year];  %# mixed continous/discrete data
y = cellstr(Origin);                        %# class labels

%# train classification decision tree
t = classregtree(x, y, 'method','classification', 'names',vars, ...
                'categorical',[2 4], 'prune','off');
view(t)

%# test
yPredicted = eval(t, x);
cm = confusionmat(y,yPredicted);           %# confusion matrix
N = sum(cm(:));
err = ( N-sum(diag(cm)) ) / N;             %# testing error

%# prune tree to avoid overfitting
tt = prune(t, 'level',3);
view(tt)

%# predict a new unseen instance
inst = [33 4 78 NaN];
prediction = eval(tt, inst)    %# pred = 'Japan'

---

Update:

The above classregtree class was made obsolete, and is superseded by ClassificationTree and RegressionTree classes in R2011a (see the fitctree and fitrtree functions, new in R2014a).

Here is the updated example, using the new functions/classes:

t = fitctree(x, y, 'PredictorNames',vars, ...
    'CategoricalPredictors',{'Cylinders', 'Model_Year'}, 'Prune','off');
view(t, 'mode','graph')

y_hat = predict(t, x);
cm = confusionmat(y,y_hat);

tt = prune(t, 'Level',3);
view(tt)

predict(tt, [33 4 78 NaN])