Multi-core implementation of Regularized Greedy Forest
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This software package provides a multi-core implementation of a simplified Regularized Greedy Forest (RGF) described in [RGF]. Please cite the paper if you find the software useful.
RGF is a machine learning method for building decision forests that have been used to win some kaggle competitions. In our experience it works better than gradient boosting on many relatively large datasets.
The implementation employs the following conepts described in the [RGF] paper:
However, various simplifications are made to accelerate the training speed. Therefore, unlike the original RGF program (see http://tongzhang-ml.org/software/rgf/index.html), this software does not reproduce the results in the paper.
The implementation of greedy tree node optimization employs second order Newton approximation for general loss functions. For logistic regression loss, which works especially well for many binary classification problems, this approach was considered in [PL]; for general loss functions, 2nd order approximation was considered in [ZCS].
Please see the file CHANGES for version information. The software is written in c++11, and it has been tested under linux and macos, and it may require g++ version 4.8 or above and cmake version 2.8 or above.
To install the binaries, unpackage the software into a directory.
To create the executables, do the following:
cd build/ cmake .. make make install
The following executabels will be installed under the subdirectory bin/.
You may use the option -h to show command-line options (options can also be provided in a configuration file).
Go to the subdirectory examples/, and following the instructions in README.md. The file also contains some tips for parameter tuning.
The software is distributed under the MIT license. Please read the file LICENSE.
[RGF] Rie Johnson and Tong Zhang. Learning Nonlinear Functions Using Regularized Greedy Forest, IEEE Trans. on Pattern Analysis and Machine Intelligence, 36:942-954, 2014.
[PL] Ping Li. Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost, UAI 2010.
[ZCS] Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun. A general boosting method and its application to learning ranking functions for web search, NIPS 2007.