Skip to main content

A set of python modules for machine learning and data mining

Project description

Azure Travis Codecov CircleCI Nightly wheels Black PythonVersion PyPi DOI Benchmark

https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png

scikit-learn is a Python module for machine learning built on top of SciPy and is 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 About us page for a list of core contributors.

It is currently maintained by a team of volunteers.

Website: https://scikit-learn.org

Installation

Dependencies

scikit-learn requires:

  • Python (>= 3.8)

  • NumPy (>= 1.17.3)

  • SciPy (>= 1.3.2)

  • joblib (>= 1.0.0)

  • threadpoolctl (>= 2.0.0)


Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. scikit-learn 1.0 and later require Python 3.7 or newer. scikit-learn 1.1 and later require Python 3.8 or newer.

Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with “Display”) require Matplotlib (>= 3.1.2). For running the examples Matplotlib >= 3.1.2 is required. A few examples require scikit-image >= 0.14.5, a few examples require pandas >= 1.0.5, some examples require seaborn >= 0.9.0.

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 -c conda-forge scikit-learn

The documentation includes more detailed installation instructions.

Changelog

See the changelog for a history of notable changes to scikit-learn.

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

Contributing

To learn more about making a contribution to scikit-learn, please see our Contributing guide.

Testing

After installation, you can launch the test suite from outside the source directory (you will need to have pytest >= 5.0.1 installed):

pytest sklearn

See the web page https://scikit-learn.org/dev/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: https://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 About us page for a list of core 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: https://scikit-learn.org/stable/about.html#citing-scikit-learn

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-1.1.1.tar.gz (6.8 MB view details)

Uploaded Source

Built Distributions

scikit_learn-1.1.1-cp310-cp310-win_amd64.whl (7.3 MB view details)

Uploaded CPython 3.10 Windows x86-64

scikit_learn-1.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30.4 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

scikit_learn-1.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (30.0 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

scikit_learn-1.1.1-cp310-cp310-macosx_12_0_arm64.whl (7.7 MB view details)

Uploaded CPython 3.10 macOS 12.0+ ARM64

scikit_learn-1.1.1-cp310-cp310-macosx_10_13_x86_64.whl (8.6 MB view details)

Uploaded CPython 3.10 macOS 10.13+ x86-64

scikit_learn-1.1.1-cp39-cp39-win_amd64.whl (7.4 MB view details)

Uploaded CPython 3.9 Windows x86-64

scikit_learn-1.1.1-cp39-cp39-win32.whl (6.6 MB view details)

Uploaded CPython 3.9 Windows x86

scikit_learn-1.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30.8 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

scikit_learn-1.1.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (30.4 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

scikit_learn-1.1.1-cp39-cp39-macosx_12_0_arm64.whl (7.7 MB view details)

Uploaded CPython 3.9 macOS 12.0+ ARM64

scikit_learn-1.1.1-cp39-cp39-macosx_10_13_x86_64.whl (8.6 MB view details)

Uploaded CPython 3.9 macOS 10.13+ x86-64

scikit_learn-1.1.1-cp38-cp38-win_amd64.whl (7.3 MB view details)

Uploaded CPython 3.8 Windows x86-64

scikit_learn-1.1.1-cp38-cp38-win32.whl (6.6 MB view details)

Uploaded CPython 3.8 Windows x86

scikit_learn-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (31.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

scikit_learn-1.1.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (30.8 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

scikit_learn-1.1.1-cp38-cp38-macosx_12_0_arm64.whl (7.6 MB view details)

Uploaded CPython 3.8 macOS 12.0+ ARM64

scikit_learn-1.1.1-cp38-cp38-macosx_10_13_x86_64.whl (8.5 MB view details)

Uploaded CPython 3.8 macOS 10.13+ x86-64

File details

Details for the file scikit-learn-1.1.1.tar.gz.

File metadata

  • Download URL: scikit-learn-1.1.1.tar.gz
  • Upload date:
  • Size: 6.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.13

File hashes

Hashes for scikit-learn-1.1.1.tar.gz
Algorithm Hash digest
SHA256 3e77b71e8e644f86c8b5be7f1c285ef597de4c384961389ee3e9ca36c445b256
MD5 cc06a851ec79e6a3d48d3f2c765bdd7a
BLAKE2b-256 4111e931951f048908ceaf2423db48ca6ad10e0b818c2960a3bc2dacb4fa4c1d

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 542ccd2592fe7ad31f5c85fed3a3deb3e252383960a85e4b49a629353fffaba4
MD5 544386bc15938ce71c0a29f31a2351a6
BLAKE2b-256 d33675da0dee6f69439ee49fb97ed6f9d859e8e3ba0cdc4cf12e8b411df9ccf4

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 47464c110eaa9ed9d1fe108cb403510878c3d3a40f110618d2a19b2190a3e35c
MD5 5c28c296602e28c599e5dc9076e15221
BLAKE2b-256 43bc7130ffd49a1cf72659c61eb94d8f037bc5502c94866f407c0219d929e758

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 33cf061ed0b79d647a3e4c3f6c52c412172836718a7cd4d11c1318d083300133
MD5 07ea926d3074cc586ab667bf41fbfe4d
BLAKE2b-256 79b278bd6b9705296a8030c398619c9dedaa0724199be800955a7c18a1e6a3ba

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp310-cp310-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp310-cp310-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 723cdb278b1fa57a55f68945bc4e501a2f12abe82f76e8d21e1806cbdbef6fc5
MD5 c4bd2dfcfc26d50fc1dcbe435e0460fc
BLAKE2b-256 2b1b1dede48e78360ee3faa96835c9b896ef792fd8939740bae07e9db8c1bbd8

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp310-cp310-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp310-cp310-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 102f51797cd8944bf44a038d106848ddf2804f2c1edf7aea45fba81a4fdc4d80
MD5 1cc7de32446d5565cf2354f3861e2634
BLAKE2b-256 371843dc30ac452e2f2a49db5b8b051d926910e09f64867ff14f4fa1d2d1377a

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 45c0f6ae523353f1d99b85469d746f9c497410adff5ba8b24423705b6956a86e
MD5 1dad74d72af565e6a63e5b0f523aef2b
BLAKE2b-256 9dbbfdc2d993543bbdae6edbea546fc8787359f46e2fe355a04a0a587db25124

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp39-cp39-win32.whl.

File metadata

  • Download URL: scikit_learn-1.1.1-cp39-cp39-win32.whl
  • Upload date:
  • Size: 6.6 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.13

File hashes

Hashes for scikit_learn-1.1.1-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 22145b60fef02e597a8e7f061ebc7c51739215f11ce7fcd2ca9af22c31aa9f86
MD5 d17d326092c0f6babad0f30615a226e2
BLAKE2b-256 537c78f5f8d215944b48ce3214fb67a2b7775ae0d08f71b272c4bc3969af3c53

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c2dad2bfc502344b869d4a3f4aa7271b2a5f4fe41f7328f404844c51612e2c58
MD5 19a7b12f975e3afc67ac94752f1f6d44
BLAKE2b-256 62cb49d4c9d3505b0dd062f49c4f573995977876cc556c658caffcfcd9043ea8

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 8ff56d07b9507fbe07ca0f4e5c8f3e171f74a429f998da03e308166251316b34
MD5 19f7ba5c9b3282c73ca42bf74b5c655a
BLAKE2b-256 21f108f5e313c028bfce28abc068ba5b6633ed95b767441b6e5271249ae65601

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp39-cp39-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp39-cp39-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 8fe80df08f5b9cee5dd008eccc672e543976198d790c07e5337f7dfb67eaac05
MD5 850b5a36bde6c32c03b377754b582197
BLAKE2b-256 646e551cc282a08755a5bce21382d196bf45205a08a44c0955875690d28acf6e

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp39-cp39-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp39-cp39-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 0403ad13f283e27d43b0ad875f187ec7f5d964903d92d1ed06c51439560ecea0
MD5 6556258fe3d425b8e22dd175dd7db58c
BLAKE2b-256 553c9d86c9d7c1454857bf9141ed23ddf48d40a7398c6dbf17bd7f8eb5025b93

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 f2d5b5d6e87d482e17696a7bfa03fe9515fdfe27e462a4ad37f3d7774a5e2fd6
MD5 cc0ebc5246a64c6bce2f5c31b45ce14d
BLAKE2b-256 409073b54af0f59f813753b4f8305439476a77d73df2d1807a6f26d6da0d2cbc

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp38-cp38-win32.whl.

File metadata

  • Download URL: scikit_learn-1.1.1-cp38-cp38-win32.whl
  • Upload date:
  • Size: 6.6 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.8.13

File hashes

Hashes for scikit_learn-1.1.1-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 e9d228ced1214d67904f26fb820c8abbea12b2889cd4aa8cda20a4ca0ed781c1
MD5 5820cd82f923d496491c2a32df911cc0
BLAKE2b-256 7a3a9bcb85c2cb8a7676fcdfeabf63614ababc088917660c47d8e33bce22cb41

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b928869072366dc138762fe0929e7dc88413f8a469aebc6a64adc10a9226180c
MD5 0bc3d2b53cd76cf2a1df5fe2fbd626f1
BLAKE2b-256 727dcbcad2588a4baf1661e43005a9c35a955ab38e247a943715d90a7c96e6b3

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e851f8874398dcd50d1e174e810e9331563d189356e945b3271c0e19ee6f4d6f
MD5 4e8828c397f41acca019ef16dcfc2794
BLAKE2b-256 58be06987c1268a5c6beea0fea7b3c25eb52839fa23693ab2f92b80721d78554

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp38-cp38-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp38-cp38-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 b2db720e13e697d912a87c1a51194e6fb085dc6d8323caa5ca51369ca6948f78
MD5 d395c4a46a539ce82a80fa680ed5bbd6
BLAKE2b-256 0fae9d01b5419ae47a429215ff1135118503ae3d119e14b73ae7596ddc1cdf03

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-1.1.1-cp38-cp38-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.1.1-cp38-cp38-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 3be10d8d325821ca366d4fe7083d87c40768f842f54371a9c908d97c45da16fc
MD5 55f2a062487e7cbfd13e86e10f72341c
BLAKE2b-256 dac9bb0a0ba39f66f90dd99bf8f32ee3fc0597d57e2a6cfbc4ec7c627ba7ec0a

See more details on using hashes here.

Provenance

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