Random output trees
Random output trees is a python package to grow decision tree ensemble on randomized output space. The core tree implementation is based on scikit-learn 0.15.2. All provided estimators and transformers are scikit-learn compatible.
If you use this package, please cite
Joly, A., Geurts, P., & Wehenkel, L. (2014). Random forests with random projections of the output space for high dimensional multi-label classification.
ECML-PKDD 2014, Nancy, France
The paper is avaiblable at http://orbi.ulg.ac.be/handle/2268/172146.
The documentation is available at http://arjoly.github.io/random-output-trees/
The required dependencies to build the software are Python >= 2.7, NumPy >= 1.6.2, SciPy >= 0.9, scikit-learn>=0.15.2 and a working C/C++ compiler.
For running the examples Matplotlib >= 1.1.1 is required and for running the tests you need nose >= 1.1.2.
For making the documentation, Sphinx==1.2.2 and sphinx-bootstrap-theme==0.4.0 are needed.
This package uses distutils, which is the default way of installing python modules. To install in your home directory, use:
python setup.py install --user
To install for all users on Unix/Linux:
python setup.py build sudo python setup.py install
You can check the latest sources with the command:
git clone https://github.com/arjoly/random-output-trees
or if you have write privileges:
After installation, you can launch the test suite from outside the
source directory (you will need to have the
nose package installed):
$ nosetests -v random_output_trees
Copyright (c) 2014, Arnaud Joly. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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