A Random Forest implementation for MATLAB. Supports arbitrary weak learners that you can define.
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Latest commit 46aa3d5 Feb 27, 2014

README.txt

Random Forest for Matlab

This toolbox was written for my own education and to give me a chance to explore the models a bit.
It is NOT intended for any serious applications and it does not NOT do many of things you would
want a mature implementation to do, like leaf pruning. If you wish to use a strong implementation
I recommend Scikit Learn / Python. For Matlab I do not really have a recommendation.

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Usage:

Random Forests for classification: (see demo for more)
opts.classfierID= [2, 3]; % use both 2D-linear weak learners (2) and conic (3)
m= forestTrain(X, Y, opts);
yhat = forestTest(m, X);
fprintf('Training accuracy = %.2f\n', mean(yhat==Y));

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More info:

Currently contains random forests.
The Random Forest code is not industrial strength implementation.

Inspired by MSR's recent work on Random Forests:
https://research.microsoft.com/apps/pubs/default.aspx?id=155552

See http://cs.stanford.edu/~karpathy/randomForestSpiral.png
for results on spiral using 2-D linear weak learners. (Code that generates
the image is in forestdemo.m) 

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Adding your own weak learners in Ranfom Forests:

It is fairly easy to add your own weak learners. Modify:
weakTrain.m:  add another elseif statement for classf variable, and put in
              code for your weak learner. Store all variables you need during
              test time in modelCandidate
weakTest.m:   add another elseif for your classifier, and implement the decision
              procedure, using variables you stored inside model.
Now just include your new classifier when setting opts.classfierID!
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BSD Licence.