A package to build an optimal binary decision tree classifier.
|Authors:||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.
|[DL852020]||Aglin, G., Nijssen, S., Schaus, P. Learning optimal decision trees using caching branch-and-bound search. In AAAI. 2020.|
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