DESlib: A Dynamic ensemble selection library in Python

TL;DR

DESlib is an open-source Python library that implements various dynamic and static ensemble selection techniques, facilitating research and application in ensemble learning with comprehensive documentation and high code quality.

Contribution

It introduces a well-documented, high-coverage Python library for dynamic and static ensemble selection methods, supporting the research community and practitioners.

Findings

Provides implementations of multiple dynamic selection techniques

Includes static ensemble methods for comparison

Part of scikit-learn-contrib ecosystem

Abstract

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.