How can I make a decision stump using a decision tree ?

Right now, I am using fitctree and pruning the tree to level one, but beacuse the 'SplitCriterion' is 'gdi' and the 'PruneCriterion' is 'error', I can not find the best decision stump to minimize the total error. I know there is an option 'MaxNumSplits' for growing a decision tree, but this option was added on 2015 and I am using 2014 version of Matlab. So, I need to build a decision stump which minimizes the error with respects to the given weights, and I prefer to use fitctree instead of writing that from scratch. Any suggestion ?!

Here is an example that explain my problem more clearly. The correct one must look like this :

I have a full tree like the following tree and I am pruning that until (max-1) level.

Since it is not a decision stump yet, I am pruning that one level more. but it does not the same as the correct one.

And it is the code that I am using:

        MinLeafSize = 1;
        MinParentSize = 2;
        NumVariablesToSample = 'all';
        ScoreTransform = 'none';
        PruneCriterion= 'error' ;
        SplitCriterion = 'gdi';
        Weights = Pt; % Train the weak learner by weights Pt
        tree = fitctree(X,Y, 'MinLeafSize',MinLeafSize, 'MinParentSize', MinParentSize, 'NumVariablesToSample', NumVariablesToSample, ...
                             'PruneCriterion',PruneCriterion, 'SplitCriterion', SplitCriterion, 'Weights', Weights, 'ScoreTransform', ScoreTransform);
        prune_tree = prune(tree, 'Level', max(tree.PruneList)-1); % prune tree to have decision stump
        % if the prune tree still has more than one decision node (three inner nodes) use the max(tree.PruneList) to reduce it to just one node
        if length(prune_tree.NodeSize) > 3
            prune_tree = prune(tree, 'Level', max(tree.PruneList));
        end

P.S. I would like to implement AdaBoostM1 by using Decision Stumps as the weak learner. I know that there is a built in function 'fitensemble' for making an ensemble classifier using AdaBoostM1 algorithm, but I would like to implement it myself.

[MHN]

The default option for using 'fitensemble' by 'AdaBoostM1' method grows such a decision stump. Does anyone know how should we manually set the options for fitctree to have the same decision stump? I have checked the classTreeEns.Trained which shows the tree properties, but since it is a compact classification tree, the pruning information and ModelParameters are removed.

Answer 1

[Ilya]

Use

fitctree(X,Y,'minparent',size(X,1),'prune','off','mergeleaves','off')

[MHN]

Thank you very much indeed. I have just added also weights to it.

fitctree(X,Y,'minparent',size(X,1),'prune','off','mergeleaves','off', 'Weights', Weights)