Metadata-aware machine learning.
BSD-3-CLAUSE License
.. -- mode: rst --
|Travis|_ |Coverage|_ |PyPI|_ |Black|_
.. |Travis| image:: https://travis-ci.org/phausamann/sklearn-xarray.svg?branch=master .. _Travis: https://travis-ci.org/phausamann/sklearn-xarray
.. |Coverage| image:: https://coveralls.io/repos/github/phausamann/sklearn-xarray/badge.svg?branch=master .. _Coverage: https://coveralls.io/github/phausamann/sklearn-xarray?branch=master
.. |PyPI| image:: https://badge.fury.io/py/sklearn-xarray.svg .. _PyPI: https://badge.fury.io/py/sklearn-xarray
.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg .. _Black: https://github.com/psf/black
sklearn-xarray is an open-source python package that combines the n-dimensional labeled arrays of xarray_ with the machine learning and model selection tools of scikit-learn_. The package contains wrappers that allow the user to apply scikit-learn estimators to xarray types without losing their labels.
.. _scikit-learn: http://scikit-learn.org/stable/ .. _xarray: http://xarray.pydata.org
The package documentation can be found at https://phausamann.github.io/sklearn-xarray/
The package can be installed with pip
::
$ pip install sklearn-xarray
or with conda
::
$ conda install -c phausamann sklearn-xarray
The activity recognition example
_ demonstrates how to use the
package for cross-validated grid search for an activity recognition task.
You can also download the example as a jupyter notebook.
.. _activity recognition example: https://phausamann.github.io/sklearn-xarray/auto_examples/plot_activity_recognition.html
Please read the contribution guide <https://github.com/phausamann/sklearn-xarray/blob/master/.github/CONTRIBUTING.rst>
_
if you want to contribute to this project.