A package to build an optimal binary decision tree classifier.
Gaël Aglin, Siegfried Nijssen, Pierre Schaus
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.
Aglin, G., Nijssen, S., Schaus, P. Learning optimal decision trees using caching branch-and-bound search. In AAAI. 2020.
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