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 (>= 1.1.1)

  • 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 (>= 3.1.3). For running the examples Matplotlib >= 3.1.3 is required. A few examples require scikit-image >= 0.16.2, a few examples require pandas >= 1.0.5, some examples require seaborn >= 0.9.0 and plotly >= 5.10.0.

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 >= 5.3.1 installed):

pytest sklearn

See the web page https://scikit-learn.org/dev/developers/contributing.html#testing-and-improving-test-coverage 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-1.2.0.tar.gz (7.2 MB view details)

Uploaded Source

Built Distributions

scikit_learn-1.2.0-cp311-cp311-win_amd64.whl (8.2 MB view details)

Uploaded CPython 3.11Windows x86-64

scikit_learn-1.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.5 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

scikit_learn-1.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (9.1 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

scikit_learn-1.2.0-cp311-cp311-macosx_12_0_arm64.whl (8.3 MB view details)

Uploaded CPython 3.11macOS 12.0+ ARM64

scikit_learn-1.2.0-cp311-cp311-macosx_10_9_x86_64.whl (9.0 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

scikit_learn-1.2.0-cp310-cp310-win_amd64.whl (8.2 MB view details)

Uploaded CPython 3.10Windows x86-64

scikit_learn-1.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.5 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

scikit_learn-1.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (9.1 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ ARM64

scikit_learn-1.2.0-cp310-cp310-macosx_12_0_arm64.whl (8.3 MB view details)

Uploaded CPython 3.10macOS 12.0+ ARM64

scikit_learn-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl (9.0 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

scikit_learn-1.2.0-cp39-cp39-win_amd64.whl (8.3 MB view details)

Uploaded CPython 3.9Windows x86-64

scikit_learn-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.5 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

scikit_learn-1.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (9.1 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

scikit_learn-1.2.0-cp39-cp39-macosx_12_0_arm64.whl (8.3 MB view details)

Uploaded CPython 3.9macOS 12.0+ ARM64

scikit_learn-1.2.0-cp39-cp39-macosx_10_9_x86_64.whl (9.1 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

scikit_learn-1.2.0-cp38-cp38-win_amd64.whl (8.2 MB view details)

Uploaded CPython 3.8Windows x86-64

scikit_learn-1.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.7 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

scikit_learn-1.2.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (9.2 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ ARM64

scikit_learn-1.2.0-cp38-cp38-macosx_12_0_arm64.whl (8.2 MB view details)

Uploaded CPython 3.8macOS 12.0+ ARM64

scikit_learn-1.2.0-cp38-cp38-macosx_10_9_x86_64.whl (8.9 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

File details

Details for the file scikit-learn-1.2.0.tar.gz.

File metadata

  • Download URL: scikit-learn-1.2.0.tar.gz
  • Upload date:
  • Size: 7.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.16

File hashes

Hashes for scikit-learn-1.2.0.tar.gz
Algorithm Hash digest
SHA256 680b65b3caee469541385d2ca5b03ff70408f6c618c583948312f0d2125df680
MD5 417ff98ec3e6e6c5d6534ad9f55e560b
BLAKE2b-256 27a095eae31ceabeb7710a694367816edfcc0ccb001c794c14b3b234c148ae50

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 f17420a8e3f40129aeb7e0f5ee35822d6178617007bb8f69521a2cefc20d5f00
MD5 14cec9a4137ad7a384c601369e185139
BLAKE2b-256 b98662738531b1db41defda03c8d065ec9f6282ec96b82309cba7715e0e263ce

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4e1ea0bc1706da45589bcf2490cde6276490a1b88f9af208dbb396fdc3a0babf
MD5 57096f42e30e77dc4217139e95371bfa
BLAKE2b-256 b4564282c0f73a49009f30b8c60b348c71b136036f608320cfba9ea744214f71

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 fc0a72237f0c56780cf550df87201a702d3bdcbbb23c6ef7d54c19326fa23f19
MD5 fec4be204ffe466d6b74322e55c3dc3e
BLAKE2b-256 f01d07b66497eb3797091944f1340698465ca4bd1a75a5a19b6bc6c865c8f40b

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp311-cp311-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp311-cp311-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 bc7073e025b62c1067cbfb76e69d08650c6b9d7a0e7afdfa20cb92d4afe516f6
MD5 691f0df0b9d768f87c3b099e535077d8
BLAKE2b-256 b0738992b6647ca8753dbe194c3582423cd965e731e2828c3edc8de5fd64ebe6

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 5546a8894a0616e92489ef995b39a0715829f3df96e801bb55cbf196be0d9649
MD5 0fc8dbafc48197e6f4756dec68d7d628
BLAKE2b-256 08b4c122c0e7225e438ff64867e5c9eb8ec246dcd2bfe5435a9a2adb3f7e160e

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 867023a044fdfe59e5014a7fec7a3086a8928f10b5dce9382eedf4135f6709a2
MD5 694802d12d9c0c2d407660e9c505704a
BLAKE2b-256 fbbcaffe1a47dc4e29f734959a53be8ae910acb627b757403f52d9c5cc2c22e4

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 184a42842a4e698ffa4d849b6019de50a77a0aa24d26afa28fa49c9190bb144b
MD5 7dfe40bcac39becece7801daccd47b02
BLAKE2b-256 efbbb625922655b063f2c2cba49b8268dac332b78b9fa7738b9e59b04909d069

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 fd3480c982b9e616b9f76ad8587804d3f4e91b4e2a6752e7dafb8a2e1f541098
MD5 5b5b86bafd2d9a376483342f4e96f7e7
BLAKE2b-256 60cfd516a5aa2b35b6540693990452d366beec8001f37bd621c997631477c66b

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp310-cp310-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp310-cp310-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 d395730f26d8fc752321f1953ddf72647c892d8bed74fad4d7c816ec9b602dfa
MD5 99eb591ce76ccbfd0827a321206cfb3e
BLAKE2b-256 6ab1bbedcbdae2c3f67b9b14af02178996e1305cf3d064fcd32d145394d17a3b

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1beaa631434d1f17a20b1eef5d842e58c195875d2bc11901a1a70b5fe544745b
MD5 88efa8c661e7c2f8b5b52c37b20fa626
BLAKE2b-256 492c7baa1b58d0987b1c7559250d87ed072d4b883193a36333a3b722b5f11344

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 ceb0008f345188aa236e49c973dc160b9ed504a3abd7b321a0ecabcb669be0bd
MD5 ee035f35113aee149e11a63bb8bc8859
BLAKE2b-256 ba8ca211a7b42e21f525ca94630ca41c888d84e6e24f6150fb08a5f187622e79

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 de897720173b26842e21bed54362f5294e282422116b61cd931d4f5d870b9855
MD5 1d8bf356891aec25547957c72bd410b1
BLAKE2b-256 83b50436307cb4f91ba280c74746fde7c89bed7a87703a2bf6e21791f56ce6de

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e9535e867281ae6987bb80620ba14cf1649e936bfe45f48727b978b7a2dbe835
MD5 3ead4ce478dde2d4f8f082373f11a9fa
BLAKE2b-256 4f10dffb594160e9edf37fafde277933aee4c2bd19849c624c6c9541bb38341c

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp39-cp39-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp39-cp39-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 40f3ff68c505cb9d1f3693397c73991875d609da905087e00e7b4477645ec67b
MD5 0afc1562eccbf901844e33e0979f78a5
BLAKE2b-256 1a30e3f9ea2a4766a59ae4c2e1c229094d9589fb32e7027167fa9e81e080e321

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 23a88883ca60c571a06278e4726b3b51b3709cfa4c93cacbf5568b22ba960899
MD5 c8e6cfefdf1ca7b434a55adb31a4a044
BLAKE2b-256 44091ce869919aef7996869c3c339a4531ce8db16ed8d49fb1c7acd50057203e

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 da29d2e379c396a63af5ed4b671ad2005cd690ac373a23bee5a0f66504e05272
MD5 b45f33b9e511a54acd717852008081ca
BLAKE2b-256 1a734aef932bc3b85afef78310ebad9cc025f20c4d979d23c42e311b25d36166

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0834e4cec2a2e0d8978f39cb8fe1cad3be6c27a47927e1774bf5737ea65ec228
MD5 935f26c34e5b416b5d59ef9f471a43dc
BLAKE2b-256 920302d3123d9462c6325e67731e7582f96904f514ede5b0666524c1bc25c053

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 83c772fa8c64776ad769fd764752c8452844307adcf10dee3adcc43988260f21
MD5 0a083ab8a23c2382dfb591f52afbb17f
BLAKE2b-256 a053d43d4e2882499ca3492a0c2a44184e96e6e87d4f2c7c2b60e4be5967e243

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp38-cp38-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp38-cp38-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 6b63ca2b0643d30fbf9d25d93017ed3fb8351f31175d82d104bfec60cba7bb87
MD5 f438b7bc3abccc616227043abf91a7a9
BLAKE2b-256 ca3b07b7dbef252b8da7c6f613fa89a69dc34cc99a6bc34fd48a1f9ddc2ffc71

See more details on using hashes here.

File details

Details for the file scikit_learn-1.2.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-1.2.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 25ba705ee1600ffc5df1dccd8fae129d7c6836e44ffcbb52d78536c9eaf8fcf9
MD5 3eb4136949d0721058baf8d15332db82
BLAKE2b-256 480ab8049d5f2fb9d8f6960a0b1994d32529c17235d46cbaae2de15d6735ad36

See more details on using hashes here.

Supported by

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