DESlib: A Dynamic ensemble selection library in Python
This library provides a tool for researchers and practitioners in machine learning to easily access and use dynamic ensemble selection methods, but it is incremental as it packages existing techniques rather than proposing new ones.
The paper introduces DESlib, an open-source Python library that implements dynamic ensemble selection techniques, including dynamic classifier selection, dynamic ensemble selection, and static ensemble methods, with full documentation, high test coverage, and integration into scikit-learn-contrib projects.
DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) \emph{dcs}, containing the implementation of dynamic classifier selection methods (DCS); (ii) \emph{des}, containing the implementation of dynamic ensemble selection methods (DES); (iii) \emph{static}, with the implementation of static ensemble techniques. The library is fully documented (documentation available online on Read the Docs), has a high test coverage (codecov.io) and is part of the scikit-learn-contrib supported projects. Documentation, code and examples can be found on its GitHub page: https://github.com/scikit-learn-contrib/DESlib.