I would like to experiment with classification problems using boosted decision trees using Matlab. In the paper An Empirical Comparison of Supervised Learning Algorithms this technique ranked #1 with respect to the metrics the authors proposed. My question is, is there a library in Matlab for this type of supervised classification?
The function fitensemble(…) has multiple techniques which I'm finding difficult to understand. For example, the 'LSBoost' technique is relevant for regression problems while I'm interested in classification only. Moreover, in a similar question posted
this exact question was asked and answered with regards to R, not Matlab.
Best Answer
Did you take a look at the documentation for fitensemble? Here it is: http://www.mathworks.com/help/stats/fitensemble.html There is a list of ensemble algorithms for classification and regression close to the top of the page. An example at the bottom shows how to grow an ensemble of decision trees by AdaBoost.
You can find more examples and explanations on this page http://www.mathworks.com/help/stats/ensemble-methods.html
By default trees for boosting are stumps. To see all default settings, click on the templateTree link in the Learners section of the fitensemble doc page. MinLeaf and MinParent are the two parameters that control the tree size. The doc for MinParent says: For boosting, the default is the number of training observations.