Metadata-Version: 2.0
Name: skll
Version: 0.26.0
Summary: SciKit-Learn Laboratory makes it easier to run machinelearning experiments with scikit-learn.
Home-page: http://github.com/EducationalTestingService/skll
Author: Daniel Blanchard
Author-email: dblanchard@ets.org
License: BSD 3 clause
Keywords: learning scikit-learn
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Requires-Dist: scikit-learn (>=0.14)
Requires-Dist: six
Requires-Dist: PrettyTable
Requires-Dist: beautifulsoup4
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: joblib

SciKit-Learn Laboratory
-----------------------

.. image:: https://travis-ci.org/EducationalTestingService/skll.png?branch=master
   :alt: Build status
   :target: https://travis-ci.org/EducationalTestingService/skll

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.. image:: https://pypip.in/d/skll/badge.png
   :target: https://crate.io/packages/skll
   :alt: PyPI downloads

.. image:: https://pypip.in/v/skll/badge.png
   :target: https://crate.io/packages/skll
   :alt: Latest version on PyPI

.. image:: https://zenodo.org/badge/3709/EducationalTestingService/skll.png
   :target: http://dx.doi.org/10.5281/zenodo.10729
   :alt: DOI for citing SKLL

This Python package provides utilities to make it easier to run
machine learning experiments with scikit-learn.

Command-line Interface
~~~~~~~~~~~~~~~~~~~~~~

``run_experiment`` is a command-line utility for running a series of learners on
datasets specified in a configuration file. For more information about using
run_experiment (including a quick example), go
`here <https://skll.readthedocs.org/en/latest/run_experiment.html>`__.

Python API
~~~~~~~~~~

If you just want to avoid writing a lot of boilerplate learning code, you can
use our simple Python API. The main way you'll want to use the API is through
the ``load_examples`` function and the ``Learner`` class. For more details on
how to simply train, test, cross-validate, and run grid search on a variety of
scikit-learn models see
`the documentation <https://skll.readthedocs.org/en/latest/index.html>`__.

A Note on Pronunciation
~~~~~~~~~~~~~~~~~~~~~~~

SciKit-Learn Laboratory (SKLL) is pronounced "skull": that's where the learning
happens.

Requirements
~~~~~~~~~~~~

-  Python 2.7+
-  `scikit-learn <http://scikit-learn.org/stable/>`__
-  `six <https://pypi.python.org/pypi/six>`__
-  `PrettyTable <http://pypi.python.org/pypi/PrettyTable>`__
-  `BeautifulSoup 4 <http://www.crummy.com/software/BeautifulSoup/>`__
-  `Grid Map <http://pypi.python.org/pypi/gridmap>`__ (only required if you plan
   to run things in parallel on a DRMAA-compatible cluster)
-  `configparser <http://pypi.python.org/pypi/configparser>`__ (only required for
   Python 2.7)
-  `futures <http://pypi.python.org/pypi/futures>`__ (only required for Python 2.7)
-  `logutils <http://pypi.python.org/pypi/logutils>`__ (only required for Python 2.7)

Talks
~~~~~

-  *Simpler Machine Learning with SKLL*, Dan Blanchard, PyData NYC 2013 (`video <http://vimeo.com/79511496>`__ | `slides <http://www.slideshare.net/DanielBlanchard2/simple-machine-learning-with-skll>`__)

Changelog
~~~~~~~~~

See `GitHub releases <https://github.com/EducationalTestingService/skll/releases>`__.


