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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 community of contributors, with volunteers but also support from several organisations.

Website: https://scikit-learn.org

Installation

Dependencies

scikit-learn requires:

  • Python (>= 3.11)
  • NumPy (>= 1.24.1)
  • SciPy (>= 1.10.0)
  • Narwhals (>= 2.0.1)
  • joblib (>= 1.4.0)
  • threadpoolctl (>= 3.5.0)

Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with Display) require Matplotlib (>= 3.6.1). For running the examples Matplotlib >= 3.6.1 is required. A few examples require scikit-image >= 0.22.0, a few examples require pandas >= 1.5.0, some examples require seaborn >= 0.13.0 and Plotly >= 5.22.0.

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

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. See https://scikit-learn.org/dev/developers/global_configuration.html#sklearn-seed for details.

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.

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

Help and Support

Documentation

Communication

Main Channels

Developer & Support

Social Media Platforms

Resources

Citation

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

About

scikit-learn: machine learning in Python

scikit-learn.org

Resources

Readme
License

Code of conduct

Code of conduct

Contributing

Contributing

Security policy

Security policy
Activity
Custom properties

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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GitHubActionsCodecovCircleCINightly wheelsRuffPythonVersionPyPIDOIBenchmark
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 community of contributors, with volunteers but also support from several
organisations.
Website: https://scikit-learn.org
Installation
Dependencies
scikit-learn requires:
  • Python (>= 3.11)
  • NumPy (>= 1.24.1)
  • SciPy (>= 1.10.0)
  • Narwhals (>= 2.0.1)
  • joblib (>= 1.4.0)
  • threadpoolctl (>= 3.5.0)
Scikit-learn plotting capabilities (i.e., functions start with plot_ and
classes end with Display) require Matplotlib (>= 3.6.1).
For running the examples Matplotlib >= 3.6.1 is required.
A few examples require scikit-image >= 0.22.0, a few examples
require pandas >= 1.5.0, some examples require seaborn >=
0.13.0 and Plotly >= 5.22.0.
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
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. See
https://scikit-learn.org/dev/developers/global_configuration.html#sklearn-seed
for details.
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.
Note: scikit-learn was previously referred to as scikits.learn.
Help and Support
Documentation
Communication
Main Channels
Developer & Support
Social Media Platforms
Resources
Citation
If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn
About
scikit-learn: machine learning in Python
scikit-learn.org
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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GitHubActionsCodecovCircleCINightly wheelsRuffPythonVersionPyPIDOIBenchmark
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 community of contributors, with volunteers but also support from several
organisations.
Website: https://scikit-learn.org
Installation
Dependencies
scikit-learn requires:
  • Python (>= 3.11)
  • NumPy (>= 1.24.1)
  • SciPy (>= 1.10.0)
  • Narwhals (>= 2.0.1)
  • joblib (>= 1.4.0)
  • threadpoolctl (>= 3.5.0)
Scikit-learn plotting capabilities (i.e., functions start with plot_ and
classes end with Display) require Matplotlib (>= 3.6.1).
For running the examples Matplotlib >= 3.6.1 is required.
A few examples require scikit-image >= 0.22.0, a few examples
require pandas >= 1.5.0, some examples require seaborn >=
0.13.0 and Plotly >= 5.22.0.
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
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. See
https://scikit-learn.org/dev/developers/global_configuration.html#sklearn-seed
for details.
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.
Note: scikit-learn was previously referred to as scikits.learn.
Help and Support
Documentation
Communication
Main Channels
Developer & Support
Social Media Platforms
Resources
Citation
If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn
About
scikit-learn: machine learning in Python
scikit-learn.org
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
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Watchers
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Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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GitHubActionsCodecovCircleCINightly wheelsRuffPythonVersionPyPIDOIBenchmark
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 community of contributors, with volunteers but also support from several
organisations.
Website: https://scikit-learn.org
Installation
Dependencies
scikit-learn requires:
  • Python (>= 3.11)
  • NumPy (>= 1.24.1)
  • SciPy (>= 1.10.0)
  • Narwhals (>= 2.0.1)
  • joblib (>= 1.4.0)
  • threadpoolctl (>= 3.5.0)
Scikit-learn plotting capabilities (i.e., functions start with plot_ and
classes end with Display) require Matplotlib (>= 3.6.1).
For running the examples Matplotlib >= 3.6.1 is required.
A few examples require scikit-image >= 0.22.0, a few examples
require pandas >= 1.5.0, some examples require seaborn >=
0.13.0 and Plotly >= 5.22.0.
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
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. See
https://scikit-learn.org/dev/developers/global_configuration.html#sklearn-seed
for details.
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.
Note: scikit-learn was previously referred to as scikits.learn.
Help and Support
Documentation
Communication
Main Channels
Developer & Support
Social Media Platforms
Resources
Citation
If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn
About
scikit-learn: machine learning in Python
scikit-learn.org
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
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GitHubActionsCodecovCircleCINightly wheelsRuffPythonVersionPyPIDOIBenchmark
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 community of contributors, with volunteers but also support from several
organisations.
Website: https://scikit-learn.org
Installation
Dependencies
scikit-learn requires:
  • Python (>= 3.11)
  • NumPy (>= 1.24.1)
  • SciPy (>= 1.10.0)
  • Narwhals (>= 2.0.1)
  • joblib (>= 1.4.0)
  • threadpoolctl (>= 3.5.0)
Scikit-learn plotting capabilities (i.e., functions start with plot_ and
classes end with Display) require Matplotlib (>= 3.6.1).
For running the examples Matplotlib >= 3.6.1 is required.
A few examples require scikit-image >= 0.22.0, a few examples
require pandas >= 1.5.0, some examples require seaborn >=
0.13.0 and Plotly >= 5.22.0.
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
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. See
https://scikit-learn.org/dev/developers/global_configuration.html#sklearn-seed
for details.
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.
Note: scikit-learn was previously referred to as scikits.learn.
Help and Support
Documentation
Communication
Main Channels
Developer & Support
Social Media Platforms
Resources
Citation
If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn
About
scikit-learn: machine learning in Python
scikit-learn.org
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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GitHubActionsCodecovCircleCINightly wheelsRuffPythonVersionPyPIDOIBenchmark
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 community of contributors, with volunteers but also support from several
organisations.
Website: https://scikit-learn.org
Installation
Dependencies
scikit-learn requires:
  • Python (>= 3.11)
  • NumPy (>= 1.24.1)
  • SciPy (>= 1.10.0)
  • Narwhals (>= 2.0.1)
  • joblib (>= 1.4.0)
  • threadpoolctl (>= 3.5.0)
Scikit-learn plotting capabilities (i.e., functions start with plot_ and
classes end with Display) require Matplotlib (>= 3.6.1).
For running the examples Matplotlib >= 3.6.1 is required.
A few examples require scikit-image >= 0.22.0, a few examples
require pandas >= 1.5.0, some examples require seaborn >=
0.13.0 and Plotly >= 5.22.0.
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
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. See
https://scikit-learn.org/dev/developers/global_configuration.html#sklearn-seed
for details.
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.
Note: scikit-learn was previously referred to as scikits.learn.
Help and Support
Documentation
Communication
Main Channels
Developer & Support
Social Media Platforms
Resources
Citation
If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn
About
scikit-learn: machine learning in Python
scikit-learn.org
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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GitHubActionsCodecovCircleCINightly wheelsRuffPythonVersionPyPIDOIBenchmark
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 community of contributors, with volunteers but also support from several
organisations.
Website: https://scikit-learn.org
Installation
Dependencies
scikit-learn requires:
  • Python (>= 3.11)
  • NumPy (>= 1.24.1)
  • SciPy (>= 1.10.0)
  • Narwhals (>= 2.0.1)
  • joblib (>= 1.4.0)
  • threadpoolctl (>= 3.5.0)
Scikit-learn plotting capabilities (i.e., functions start with plot_ and
classes end with Display) require Matplotlib (>= 3.6.1).
For running the examples Matplotlib >= 3.6.1 is required.
A few examples require scikit-image >= 0.22.0, a few examples
require pandas >= 1.5.0, some examples require seaborn >=
0.13.0 and Plotly >= 5.22.0.
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
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. See
https://scikit-learn.org/dev/developers/global_configuration.html#sklearn-seed
for details.
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.
Note: scikit-learn was previously referred to as scikits.learn.
Help and Support
Documentation
Communication
Main Channels
Developer & Support
Social Media Platforms
Resources
Citation
If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn
About
scikit-learn: machine learning in Python
scikit-learn.org
Resources
Readme
License
Code of conduct
Code of conduct
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Contributing
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Security policy
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
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GitHubActionsCodecovCircleCINightly wheelsRuffPythonVersionPyPIDOIBenchmark
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 community of contributors, with volunteers but also support from several
organisations.
Website: https://scikit-learn.org
Installation
Dependencies
scikit-learn requires:
  • Python (>= 3.11)
  • NumPy (>= 1.24.1)
  • SciPy (>= 1.10.0)
  • Narwhals (>= 2.0.1)
  • joblib (>= 1.4.0)
  • threadpoolctl (>= 3.5.0)
Scikit-learn plotting capabilities (i.e., functions start with plot_ and
classes end with Display) require Matplotlib (>= 3.6.1).
For running the examples Matplotlib >= 3.6.1 is required.
A few examples require scikit-image >= 0.22.0, a few examples
require pandas >= 1.5.0, some examples require seaborn >=
0.13.0 and Plotly >= 5.22.0.
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
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. See
https://scikit-learn.org/dev/developers/global_configuration.html#sklearn-seed
for details.
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.
Note: scikit-learn was previously referred to as scikits.learn.
Help and Support
Documentation
Communication
Main Channels
Developer & Support
Social Media Platforms
Resources
Citation
If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn
About
scikit-learn: machine learning in Python
scikit-learn.org
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Security policy
Security policy
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages