A set of python modules for machine learning and data mining

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for adrin from gravatar.com adrin Avatar for GaelVaroquaux from gravatar.com GaelVaroquaux Avatar for glemaitre from gravatar.com glemaitre Avatar for jeremiedbb from gravatar.com jeremiedbb Avatar for jnothman from gravatar.com jnothman Avatar for ogrisel from gravatar.com ogrisel Avatar for t3kcit from gravatar.com t3kcit Avatar for thomasjpfan from gravatar.com thomasjpfan

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Project description

Travis AppVeyor Codecov CircleCI Python27 Python35 PyPi DOI

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst file for a complete list of contributors.

It is currently maintained by a team of volunteers.

Website: http://scikit-learn.org

Installation

Dependencies

scikit-learn requires:

  • Python (>= 2.7 or >= 3.3)

  • NumPy (>= 1.8.2)

  • SciPy (>= 0.13.3)

For running the examples Matplotlib >= 1.1.1 is required.

scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra Subprograms library. scikit-learn comes with a reference implementation, but the system CBLAS will be detected by the build system and used if present. CBLAS exists in many implementations; see Linear algebra libraries for known issues.

User installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip

pip install -U scikit-learn

or conda:

conda install scikit-learn

The documentation includes more detailed installation instructions.

Development

We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We’ve included some basic information in this README.

Source code

You can check the latest sources with the command:

git clone https://github.com/scikit-learn/scikit-learn.git

Setting up a development environment

Quick tutorial on how to go about setting up your environment to contribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md

Testing

After installation, you can launch the test suite from outside the source directory (you will need to have the nose package installed):

nosetests -v sklearn

Under Windows, it is recommended to use the following command (adjust the path to the python.exe program) as using the nosetests.exe program can badly interact with tests that use multiprocessing:

C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn

See the web page http://scikit-learn.org/stable/developers/advanced_installation.html#testing for more information.

Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable.

Submitting a Pull Request

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: http://scikit-learn.org/stable/developers/index.html

Project History

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst file for a complete list of contributors.

The project is currently maintained by a team of volunteers.

Note: scikit-learn was previously referred to as scikits.learn.

Help and Support

Documentation

Communication

Citation

If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for adrin from gravatar.com adrin Avatar for GaelVaroquaux from gravatar.com GaelVaroquaux Avatar for glemaitre from gravatar.com glemaitre Avatar for jeremiedbb from gravatar.com jeremiedbb Avatar for jnothman from gravatar.com jnothman Avatar for ogrisel from gravatar.com ogrisel Avatar for t3kcit from gravatar.com t3kcit Avatar for thomasjpfan from gravatar.com thomasjpfan

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

1.5.2

1.5.1

1.5.0

1.5.0rc1 pre-release

1.4.2

1.4.1.post1

1.4.0

1.4.0rc1 pre-release

1.3.2

1.3.1

1.3.0

1.3.0rc1 pre-release

1.2.2

1.2.1

1.2.0

1.2.0rc1 pre-release

1.1.3

1.1.2

1.1.1

1.1.0

1.0.2

1.0.1

1.0

0.24.2

0.24.1

0.24.0

0.23.2

0.23.1

0.23.0

0.22.2.post1

0.22.2

0.22.1

0.22

0.21.3

0.21.2

0.21.1

0.21.0

0.20.4

0.20.3

0.20.2

0.20.1

0.20.0

0.19.2

This version

0.19.1

0.19.0

0.18.2

0.18.1

0.18

0.17.1

0.17

0.16.1

0.16.0

0.15.2

0.15.1

0.15.0

0.14.1

0.14

0.13.1

0.13

0.12.1

0.12

0.11

0.10

0.9

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scikit-learn-0.19.1.tar.gz (9.5 MB view hashes)

Uploaded Source

Built Distributions

scikit_learn-0.19.1-cp36-cp36m-win_amd64.whl (4.3 MB view hashes)

Uploaded CPython 3.6m Windows x86-64

scikit_learn-0.19.1-cp36-cp36m-win32.whl (3.9 MB view hashes)

Uploaded CPython 3.6m Windows x86

scikit_learn-0.19.1-cp36-cp36m-manylinux1_x86_64.whl (12.4 MB view hashes)

Uploaded CPython 3.6m

scikit_learn-0.19.1-cp36-cp36m-manylinux1_i686.whl (11.6 MB view hashes)

Uploaded CPython 3.6m

scikit_learn-0.19.1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (7.6 MB view hashes)

Uploaded CPython 3.6m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

scikit_learn-0.19.1-cp35-cp35m-win_amd64.whl (4.3 MB view hashes)

Uploaded CPython 3.5m Windows x86-64

scikit_learn-0.19.1-cp35-cp35m-win32.whl (3.9 MB view hashes)

Uploaded CPython 3.5m Windows x86

scikit_learn-0.19.1-cp35-cp35m-manylinux1_x86_64.whl (12.2 MB view hashes)

Uploaded CPython 3.5m

scikit_learn-0.19.1-cp35-cp35m-manylinux1_i686.whl (11.4 MB view hashes)

Uploaded CPython 3.5m

scikit_learn-0.19.1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (7.5 MB view hashes)

Uploaded CPython 3.5m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

scikit_learn-0.19.1-cp34-cp34m-win_amd64.whl (4.3 MB view hashes)

Uploaded CPython 3.4m Windows x86-64

scikit_learn-0.19.1-cp34-cp34m-win32.whl (4.0 MB view hashes)

Uploaded CPython 3.4m Windows x86

scikit_learn-0.19.1-cp34-cp34m-manylinux1_x86_64.whl (12.4 MB view hashes)

Uploaded CPython 3.4m

scikit_learn-0.19.1-cp34-cp34m-manylinux1_i686.whl (11.6 MB view hashes)

Uploaded CPython 3.4m

scikit_learn-0.19.1-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (7.5 MB view hashes)

Uploaded CPython 3.4m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

scikit_learn-0.19.1-cp27-cp27mu-manylinux1_x86_64.whl (12.2 MB view hashes)

Uploaded CPython 2.7mu

scikit_learn-0.19.1-cp27-cp27mu-manylinux1_i686.whl (11.4 MB view hashes)

Uploaded CPython 2.7mu

scikit_learn-0.19.1-cp27-cp27m-win_amd64.whl (4.5 MB view hashes)

Uploaded CPython 2.7m Windows x86-64

scikit_learn-0.19.1-cp27-cp27m-win32.whl (4.1 MB view hashes)

Uploaded CPython 2.7m Windows x86

scikit_learn-0.19.1-cp27-cp27m-manylinux1_x86_64.whl (12.2 MB view hashes)

Uploaded CPython 2.7m

scikit_learn-0.19.1-cp27-cp27m-manylinux1_i686.whl (11.4 MB view hashes)

Uploaded CPython 2.7m

scikit_learn-0.19.1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (8.0 MB view hashes)

Uploaded CPython 2.7m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

scikit-learn-0.19.1.win-amd64-py3.5.exe (4.9 MB view hashes)

Uploaded Source

scikit-learn-0.19.1.win-amd64-py2.7.exe (4.7 MB view hashes)

Uploaded Source

scikit-learn-0.19.1.win32-py3.5.exe (4.4 MB view hashes)

Uploaded Source

scikit-learn-0.19.1.win32-py2.7.exe (4.3 MB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page