karpathy / Random Forest Matlab

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.

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));

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)

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!

BSD Licence.