  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}.
