Fast Ensembles of Sparse Trees
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FEST, short for Fast Ensembles of Sparse Trees, is a piece of software for
learning various types of decision tree committees from high dimensional sparse
data. FEST is written in C and can do bagging, boosting, random forests plus a
few more tree based learning algorithms (see the FAQ below).


The latest version of FEST can be found at

The program is free for scientific and educational use. See the file
LICENSE.txt that comes with the above distribution.


To compile FEST, you just need to issue the following commands:

                        tar -zxvf fest.tar.gz
                        cd fest

Then copy the executables festlearn and festclassify to a directory in your


FEST consists of a learning program (festlearn) and a classification program
(festclassify). The learning program takes as input a set of examples and
outputs the model it learned. The classification program takes as input a model
and a set of examples and outputs the predictions of the model on the examples.

festlearn is called this way:

            festlearn [options] data model
            Available options:
                -c <int>  : committee type:
                            1 bagging
                            2 boosting (default)
                            3 random forest
                -d <int>  : maximum depth of the trees (default: 1000)
                -e        : report out of bag estimates (default: no)
                -n <float>: relative weight for the negative class (default: 1)
                -p <float>: parameter for random forests: (default: 1)
                            (ratio of features considered over sqrt(features))
                -t <int>  : number of trees (default: 100)

The input file 'data' contains the training examples. It should be in the 
SVM-light/LIBSVM format

festclassify is called this way:

            festclassify [options] data model predictions
            Available options:
                 -t <int>  : number of trees to use (default: 0 = all)

The input file 'data' contains the test examples and should be in the same
format as the training examples.

For each test example, the prediction of the model (stored in the 'model' file)
is written to the 'predictions' file.


Q:How to grow a single tree?

A:Growing one tree is the same as boosting for one iteration:

            festlearn -t 1 single.tree

Q:Does FEST support boosted stumps?

A:Boosted stumps are just boosted trees with a maximum depth of 1

            festlearn -d 1 -t 1000 1000.boosted.stumps

Q:Can FEST handle continuous/ordinal attributes?

A:Yes. Ordinal attributes are modeled as continuous attributes. In fact, all
attributes are considered continuous unless they only take the values 0 and 1.
Notice that the running time of the program increases as the number of
continuous attributes in your dataset increases. This is for two reasons: a)
unlike a binary atrribute, a continuous attribute has to be considered at every
node even if it has been used before. b) For continuous attributes one has to
determine an appropriate threshold which requires some amount of searching.