Pycobra: A Python Toolbox for Ensemble Learning and Visualisation

TL;DR

pycobra is a Python library that simplifies ensemble learning and visualization, offering multiple algorithms, a flexible interface, and visualization tools, all compatible with scikit-learn and open-source.

Contribution

It introduces a versatile Python toolbox for ensemble learning and visualization, integrating various algorithms and providing a unified, user-friendly interface.

Findings

Supports regression and classification tasks

Includes visualization tools like Voronoi tessellations

Fully compatible with scikit-learn ecosystem

Abstract

We introduce \texttt{pycobra}, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Python libraries (as long as a \texttt{predict} method is given), and visualisation tools such as Voronoi tessellations. \texttt{pycobra} is fully \texttt{scikit-learn} compatible and is released under the MIT open-source license. \texttt{pycobra} can be downloaded from the Python Package Index (PyPi) and Machine Learning Open Source Software (MLOSS). The current version (along with Jupyter notebooks, extensive documentation, and continuous integration tests) is available at \href{https://github.com/bhargavvader/pycobra}{https://github.com/bhargavvader/pycobra} and official documentation website is \href{https://modal.lille.inria.fr/pycobra}{https://modal.lille.inria.fr/pycobra}.