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Distributed Training of Decision Trees and Random Forests

Following divide and conquer, this system treats each tree node to construct as a task, and parallelizes all tasks as much as possible following the idea of divide and conquer.

This allows it to use all CPU cores in a cluster to train tree models over big data. We require data to be kept on Hadoop Distributed File System for parallel loading. For more details on how to run the system, please read the file "ReadMe.txt".

Configure the Running Environment

Please follow the documentation here to deploy: https://yanlab19870714.github.io/yanda/gthinker/deploy.html We additionally require you to install the Boost C++ library. You may need to update the Makefiles to run on your platform.

Contact

Da Yan: https://yanlab19870714.github.io/yanda

Video Demo: https://www.youtube.com/watch?v=4DnLv_OFlIg

Email: yanda@uab.edu

Contributors

CHOWDHURY, Md Mashiur Rahman (Mashiur)

YAN, Da (Daniel)

GUO, Guimu

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