scikit-learn

A set of python modules for machine learning and data mining

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Project links

Project description

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 (>= 3.9)
NumPy (>= 1.19.5)
SciPy (>= 1.6.0)
joblib (>= 1.2.0)
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 (>= 3.3.4). For running the examples Matplotlib >= 3.3.4 is required. A few examples require scikit-image >= 0.17.2, a few examples require pandas >= 1.1.5, some examples require seaborn >= 0.9.0 and plotly >= 5.14.0.

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.

Important links

Official source code repo: https://github.com/scikit-learn/scikit-learn
Download releases: https://pypi-hypernode.com/project/scikit-learn/
Issue tracker: https://github.com/scikit-learn/scikit-learn/issues

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 >= 7.1.2 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

HTML documentation (stable release): https://scikit-learn.org
HTML documentation (development version): https://scikit-learn.org/dev/
FAQ: https://scikit-learn.org/stable/faq.html

Communication

Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
Gitter: https://gitter.im/scikit-learn/scikit-learn
Logos & Branding: https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
Blog: https://blog.scikit-learn.org
Calendar: https://blog.scikit-learn.org/calendar/
Twitter: https://twitter.com/scikit_learn
Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
Github Discussions: https://github.com/scikit-learn/scikit-learn/discussions
Website: https://scikit-learn.org
LinkedIn: https://www.linkedin.com/company/scikit-learn
YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
Facebook: https://www.facebook.com/scikitlearnofficial/
Instagram: https://www.instagram.com/scikitlearnofficial/
TikTok: https://www.tiktok.com/@scikit.learn

Citation

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

Project details

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Project links

Release history Release notifications | RSS feed

1.5.2

Sep 11, 2024

1.5.1

Jul 3, 2024

1.5.0

May 21, 2024

1.5.0rc1 pre-release

May 6, 2024

1.4.2

Apr 9, 2024

1.4.1.post1

Feb 16, 2024

This version

1.4.0

Jan 18, 2024

1.4.0rc1 pre-release

Dec 20, 2023

1.3.2

Oct 23, 2023

1.3.1

Sep 21, 2023

1.3.0

Jun 30, 2023

1.3.0rc1 pre-release

Jun 15, 2023

1.2.2

Mar 9, 2023

1.2.1

Jan 24, 2023

1.2.0

Dec 8, 2022

1.2.0rc1 pre-release

Nov 28, 2022

1.1.3

Oct 26, 2022

1.1.2

Aug 5, 2022

1.1.1

May 19, 2022

1.1.0

May 12, 2022

1.0.2

Dec 25, 2021

1.0.1

Oct 25, 2021

1.0

Sep 24, 2021

0.24.2

Apr 28, 2021

0.24.1

Jan 19, 2021

0.24.0

Dec 22, 2020

0.23.2

Aug 4, 2020

0.23.1

May 19, 2020

0.23.0

May 12, 2020

0.22.2.post1

Mar 4, 2020

0.22.2

Feb 28, 2020

0.22.1

Jan 2, 2020

0.22

Dec 3, 2019

0.21.3

Jul 30, 2019

0.21.2

May 24, 2019

0.21.1

May 15, 2019

0.21.0

May 10, 2019

0.20.4

Jul 30, 2019

0.20.3

Mar 1, 2019

0.20.2

Dec 19, 2018

0.20.1

Nov 23, 2018

0.20.0

Sep 26, 2018

0.19.2

Jul 15, 2018

0.19.1

Oct 23, 2017

0.19.0

Aug 11, 2017

0.18.2

Jun 21, 2017

0.18.1

Nov 13, 2016

0.18

Sep 28, 2016

0.17.1

Feb 18, 2016

0.17

Nov 5, 2015

0.16.1

Apr 14, 2015

0.16.0

Mar 27, 2015

0.15.2

Sep 5, 2014

0.15.1

Aug 1, 2014

0.15.0

Jul 15, 2014

0.14.1

Aug 8, 2013

0.14

Aug 7, 2013

0.13.1

Feb 23, 2013

0.13

Jan 21, 2013

0.12.1

Oct 8, 2012

0.12

Sep 4, 2012

0.11

May 7, 2012

0.10

Jan 11, 2012

0.9

Sep 21, 2011

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scikit-learn 1.4.0

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Algorithm	Hash digest
SHA256	`d3d75343940e7bf9b85c830c93d34039fa015eeb341c5c0b4cd7a90dadfe00d4`
MD5	`2d2566b62151d0525f9cb28b589db872`
BLAKE2b-256	`f628b569523552a11b49dc4d33952f43dedb23792fe8ce2f2151d070d615861a`

Algorithm	Hash digest
SHA256	`a0e2427d9ef46477625ab9b55c1882844fe6fc500f418c3f8e650200182457bc`
MD5	`cd20333342bda38f963808eee494b7e7`
BLAKE2b-256	`b914b37b415be8179dff311fea26d1cedf59be2d79441486409c1bf8fde63085`

Algorithm	Hash digest
SHA256	`d439c584e58434d0350701bd33f6c10b309e851fccaf41c121aed55f6851d8cf`
MD5	`4dc1e7c9e2c97b21375472ccbe0811a9`
BLAKE2b-256	`094145c5f19791af30e9b1df3d263a8bd1d914c3eb0aa89a52be7d802a18fcfe`

Algorithm	Hash digest
SHA256	`349669b01435bc4dbf25c6410b0892073befdaec52637d1a1d1ff53865dc8db3`
MD5	`d8b313fea99dd5076cb230ee3e1b463d`
BLAKE2b-256	`4d3a434ae6d126fec4603375a92680aca8adb37839c3b17e6833c28ba2d58437`

Algorithm	Hash digest
SHA256	`e7eef6ea2ed289af40e88c0be9f7704ca8b5de18508a06897c3fe21e0905efdf`
MD5	`d751579edd227f38a0e6363290e29ac3`
BLAKE2b-256	`60ec9e0d1b38d30ba82e32763158bb9c1b7aaaf8b104268251db9e734d2adb0a`

Algorithm	Hash digest
SHA256	`0db8e22c42f7980fe5eb22069b1f84c48966f3e0d23a01afde5999e3987a2501`
MD5	`30888b8ab02c9ccd42cdc39454350b3f`
BLAKE2b-256	`a8e93e4879974a7c4dcaca2a746dde3df08d0ae8f14c74b03591616ce5f0a8b1`

Algorithm	Hash digest
SHA256	`2b465dd1dcd237b7b1dcd1a9048ccbf70a98c659474324fa708464c3a2533fad`
MD5	`eab634cc13b35141d7cefc053e4bbdb4`
BLAKE2b-256	`5bbe208f17ce87a5e55094b0e8ffd55b06919ab9b56e7e4ce2a64cd9095ec5d2`

Algorithm	Hash digest
SHA256	`c408b46b2fd61952d519ea1af2f8f0a7a703e1433923ab1704c4131520b2083b`
MD5	`ec579529a0b6e66e9a0f5cb861af5fd7`
BLAKE2b-256	`a2a6d923a158cdb76d3a1952267041b89482e456e52096311c28eb4187c51e09`

Algorithm	Hash digest
SHA256	`d1f6bce875ac2bb6b52514f67c185c564ccd299a05b65b7bab091a4c13dde12d`
MD5	`01052a292eaf3479eb898f32dd670fe6`
BLAKE2b-256	`75951917ac1ac6de32a087734833e7ecd5631f102cc35020daa2bee8558eca89`

Algorithm	Hash digest
SHA256	`a8341eabdc754d5ab91641a7763243845e96b6d68e03e472531e88a4f1b09f21`
MD5	`2546dabf002477afa51de7587917e8dc`
BLAKE2b-256	`b248ebff4e2b434865737d95b85366b339c0bf16c5865ab6f314bb0f28cbb695`