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.. |Travis| image:: https://travis-ci.org/akusok/scikit-elm.svg?branch=master .. _Travis: https://travis-ci.org/akusok/scikit-elm
.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/957kf3r6eqcnbspp?svg=true .. _AppVeyor: https://ci.appveyor.com/project/glemaitre/project-template
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.. _scikit-learn: https://scikit-learn.org .. _dask: https://dask.org .. _plaidml: https://github.com/plaidml/plaidml/blob/master/docs/install.md#macos
scikit-elm is a scikit-learn_ compatible Extreme Learning Machine (ELM) regressor/classifier.
It features very high degree of model flexibility: dynamically added classes,
partial_fit without performance penalties, wide data format compatibility,
optimization and parameter selection without full re-training.
Big Data and out-of-core learning support through dask_-powered backend. GPU acceleration support with NVidia hardware, and on macOS through plaidml_.
Toolbox is in active development, initial release soon.
FAQs
Extreme Learning Machine regressor/classifer compatible with scikit-learn
We found that scikit-elm demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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