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A package to build an optimal binary decision tree classifier.

Project description

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Authors:Gaël Aglin, Siegfried Nijssen, Pierre Schaus

Relevant paper: [DL852020]

This project implements an algorithm for inferring optimal binary decision trees. It is scikit-learn compatible and can be used in combination with scikit-learn. As a scikit-learn classifier, it implements the methods “fit” and “predict”.

This tool can be installed in two ways:

  • download the source from github and install using the command python3 setup.py install in the root folder
  • install from pip by using the command pip install dl8.5 in the console

Disclaimer: The compilation of the project has been tested with C++ compilers on the Linux and MacOS operating systems; Windows is not yet supported.

[DL852020]Aglin, G., Nijssen, S., Schaus, P. Learning optimal decision trees using caching branch-and-bound search. In AAAI. 2020.

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