A multi-output/multi-label and stream data mining framework.
Project description
A machine learning framework for multi-output/multi-label and stream data. Inspired by MOA and MEKA, following scikit-learn's philosophy.
matplotlib backend considerations
- You may need to change your matplotlib backend, because not all backends work in all machines.
- If this is the case you need to check
matplotlib's configuration.
In the matplotlibrc file you will need to change the line:
to:backend : Qt5Agg
backend : another backend that works on your machine
- The Qt5Agg backend should work with most machines, but a change may be needed.
Jupyter Notebooks
In order to display plots from scikit-multiflow
within a Jupyter Notebook we need to define the proper mathplotlib
backend to use. This is done via a magic command at the beginning of the Notebook:
%matplotlib notebook
JupyterLab is the next-generation user interface for Jupyter, currently in beta, it can display interactive plots with some caveats. If you use JupyterLab then the current solution is to use the jupyter-matplotlib extension:
%matplotlib widget
Citing scikit-multiflow
If you want to cite scikit-multiflow
in a scientific publication, please use the following Bibtex entry:
@article{skmultiflow,
author = {Jacob Montiel and Jesse Read and Albert Bifet and Talel Abdessalem},
title = {Scikit-Multiflow: A Multi-output Streaming Framework },
journal = {Journal of Machine Learning Research},
year = {2018},
volume = {19},
number = {72},
pages = {1-5},
url = {http://jmlr.org/papers/v19/18-251.html}
}
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