doc/install.rst
.. _installation-instructions:
There are different ways to install scikit-learn:
:ref:Install the latest official release <install_official_release>. This
is the best approach for most users. It will provide a stable version
and pre-built packages are available for most platforms.
Install the version of scikit-learn provided by your
:ref:operating system or Python distribution <install_by_distribution>.
This is a quick option for those who have operating systems or Python
distributions that distribute scikit-learn.
It might not provide the latest release version.
:ref:Install a nightly build <install_nightly_builds>. This is the quickest way to
try a new feature that will be shipped in the next release (that is, a
feature from a pull-request that was recently merged to the main branch); or to check
whether a bug you encountered has been fixed since the last release.
:ref:Building the package from source <setup_development_environment>.
This is mainly needed by users who wish to contribute to the project, as this allows
to install an editable version of the project.
.. _install_official_release:
.. raw:: html
<style> /* Show caption on large screens */ @media screen and (min-width: 960px) { .install-instructions .sd-tab-set { --tab-caption-width: 20%; } .install-instructions .sd-tab-set.tabs-os::before { content: "Operating System"; } .install-instructions .sd-tab-set.tabs-package-manager::before { content: "Package Manager"; } } </style>.. div:: install-instructions
.. tab-set:: :class: tabs-os
.. tab-item:: Windows
:class-label: tab-4
.. tab-set::
:class: tabs-package-manager
.. tab-item:: pip
:class-label: tab-6
:sync: package-manager-pip
Install the 64-bit version of Python 3, for instance from the
`official website <https://www.python.org/downloads/windows/>`__.
Now create a `virtual environment (venv)
<https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
Note that the virtual environment is optional but strongly recommended, in
order to avoid potential conflicts with other packages.
.. prompt:: powershell
python -m venv sklearn-env
sklearn-env\Scripts\activate # activate
pip install -U scikit-learn
In order to check your installation, you can use:
.. prompt:: powershell
python -m pip show scikit-learn # show scikit-learn version and location
python -m pip freeze # show all installed packages in the environment
python -c "import sklearn; sklearn.show_versions()"
.. tab-item:: conda
:class-label: tab-6
:sync: package-manager-conda
.. include:: ./install_instructions_conda.rst
.. tab-item:: MacOS
:class-label: tab-4
.. tab-set::
:class: tabs-package-manager
.. tab-item:: pip
:class-label: tab-6
:sync: package-manager-pip
Install Python 3 using `homebrew <https://brew.sh/>`_ (`brew install python`)
or by manually installing the package from the `official website
<https://www.python.org/downloads/macos/>`__.
Now create a `virtual environment (venv)
<https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
Note that the virtual environment is optional but strongly recommended, in
order to avoid potential conflicts with other packages.
.. prompt:: bash
python -m venv sklearn-env
source sklearn-env/bin/activate # activate
pip install -U scikit-learn
In order to check your installation, you can use:
.. prompt:: bash
python -m pip show scikit-learn # show scikit-learn version and location
python -m pip freeze # show all installed packages in the environment
python -c "import sklearn; sklearn.show_versions()"
.. tab-item:: conda
:class-label: tab-6
:sync: package-manager-conda
.. include:: ./install_instructions_conda.rst
.. tab-item:: Linux
:class-label: tab-4
.. tab-set::
:class: tabs-package-manager
.. tab-item:: pip
:class-label: tab-6
:sync: package-manager-pip
Python 3 is usually installed by default on most Linux distributions. To
check if you have it installed, try:
.. prompt:: bash
python3 --version
pip3 --version
If you don't have Python 3 installed, please install `python3` and
`python3-pip` from your distribution's package manager.
Now create a `virtual environment (venv)
<https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
Note that the virtual environment is optional but strongly recommended, in
order to avoid potential conflicts with other packages.
.. prompt:: bash
python3 -m venv sklearn-env
source sklearn-env/bin/activate # activate
pip3 install -U scikit-learn
In order to check your installation, you can use:
.. prompt:: bash
python3 -m pip show scikit-learn # show scikit-learn version and location
python3 -m pip freeze # show all installed packages in the environment
python3 -c "import sklearn; sklearn.show_versions()"
.. tab-item:: conda
:class-label: tab-6
:sync: package-manager-conda
.. include:: ./install_instructions_conda.rst
Using an isolated environment such as pip venv or conda makes it possible to install a specific version of scikit-learn with pip or conda and its dependencies independently of any previously installed Python packages. In particular under Linux it is discouraged to install pip packages alongside the packages managed by the package manager of the distribution (apt, dnf, pacman...).
Note that you should always remember to activate the environment of your choice prior to running any Python command whenever you start a new terminal session.
If you have not installed NumPy or SciPy yet, you can also install these using conda or pip. When using pip, please ensure that binary wheels are used, and NumPy and SciPy are not recompiled from source, which can happen when using particular configurations of operating system and hardware (such as Linux on a Raspberry Pi).
Scikit-learn plotting capabilities (i.e., functions starting with plot\_
and classes ending with Display) require Matplotlib. The examples require
Matplotlib and some examples require scikit-image, pandas, or seaborn. The
minimum version of scikit-learn dependencies are listed below along with its
purpose.
.. include:: min_dependency_table.rst
.. warning::
Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.
Scikit-learn 0.21 supported Python 3.5—3.7.
Scikit-learn 0.22 supported Python 3.5—3.8.
Scikit-learn 0.23 required Python 3.6—3.8.
Scikit-learn 0.24 required Python 3.6—3.9.
Scikit-learn 1.0 supported Python 3.7—3.10.
Scikit-learn 1.1, 1.2 and 1.3 supported Python 3.8—3.12.
Scikit-learn 1.4 and 1.5 supported Python 3.9—3.12.
Scikit-learn 1.6 supported Python 3.9—3.13.
Scikit-learn 1.7 requires Python 3.10 or newer.
.. _install_by_distribution:
Some third-party distributions provide versions of scikit-learn integrated with their package-management systems.
These can make installation and upgrading much easier for users since the integration includes the ability to automatically install dependencies (numpy, scipy) that scikit-learn requires.
The following is an incomplete list of OS and python distributions that provide their own version of scikit-learn.
Alpine Linux's package is provided through the official repositories <https://pkgs.alpinelinux.org/packages?name=py3-scikit-learn>__ as
py3-scikit-learn for Python.
It can be installed by typing the following command:
.. prompt:: bash
sudo apk add py3-scikit-learn
Arch Linux's package is provided through the official repositories <https://www.archlinux.org/packages/?q=scikit-learn>_ as
python-scikit-learn for Python.
It can be installed by typing the following command:
.. prompt:: bash
sudo pacman -S python-scikit-learn
The Debian/Ubuntu package is split in three different packages called
python3-sklearn (python modules), python3-sklearn-lib (low-level
implementations and bindings), python-sklearn-doc (documentation).
Note that scikit-learn requires Python 3, hence the need to use the python3-
suffixed package names.
Packages can be installed using apt-get:
.. prompt:: bash
sudo apt-get install python3-sklearn python3-sklearn-lib python-sklearn-doc
The Fedora package is called python3-scikit-learn for the python 3 version,
the only one available in Fedora.
It can be installed using dnf:
.. prompt:: bash
sudo dnf install python3-scikit-learn
scikit-learn is available via pkgsrc-wip <https://pkgsrc-wip.sourceforge.net/>_:
https://pkgsrc.se/math/py-scikit-learn
The MacPorts package is named py<XY>-scikit-learn,
where XY denotes the Python version.
It can be installed by typing the following
command:
.. prompt:: bash
sudo port install py312-scikit-learn
Anaconda <https://www.anaconda.com/download>_ and
Enthought Deployment Manager <https://assets.enthought.com/downloads/>_
both ship with scikit-learn in addition to a large set of scientific
python library for Windows, Mac OSX and Linux.
Anaconda offers scikit-learn as part of its free distribution.
Intel maintains an optimized x86_64 package, available in PyPI (via pip),
and in the main, conda-forge and intel conda channels:
.. prompt:: bash
conda install scikit-learn-intelex
This package has an Intel optimized version of many estimators. Whenever an alternative implementation doesn't exist, scikit-learn implementation is used as a fallback. Those optimized solvers come from the oneDAL C++ library and are optimized for the x86_64 architecture, and are optimized for multi-core Intel CPUs.
Note that those solvers are not enabled by default, please refer to the
scikit-learn-intelex <https://intel.github.io/scikit-learn-intelex/latest/what-is-patching.html>_
documentation for more details on usage scenarios. Direct export example:
.. prompt:: python >>>
from sklearnex.neighbors import NearestNeighbors
Compatibility with the standard scikit-learn solvers is checked by running the
full scikit-learn test suite via automated continuous integration as reported
on https://github.com/intel/scikit-learn-intelex. If you observe any issue
with scikit-learn-intelex, please report the issue on their
issue tracker <https://github.com/intel/scikit-learn-intelex/issues>__.
The WinPython <https://winpython.github.io/>_ project distributes
scikit-learn as an additional plugin.
If you encounter unexpected failures when installing scikit-learn, you may submit
an issue to the issue tracker <https://github.com/scikit-learn/scikit-learn/issues>_.
Before that, please also make sure to check the following common issues.
.. _windows_longpath:
It can happen that pip fails to install packages when reaching the default path
size limit of Windows if Python is installed in a nested location such as the
AppData folder structure under the user home directory, for instance::
C:\Users\username>C:\Users\username\AppData\Local\Microsoft\WindowsApps\python.exe -m pip install scikit-learn
Collecting scikit-learn
...
Installing collected packages: scikit-learn
ERROR: Could not install packages due to an OSError: [Errno 2] No such file or directory: 'C:\\Users\\username\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\sklearn\\datasets\\tests\\data\\openml\\292\\api-v1-json-data-list-data_name-australian-limit-2-data_version-1-status-deactivated.json.gz'
In this case it is possible to lift that limit in the Windows registry by
using the regedit tool:
#. Type "regedit" in the Windows start menu to launch regedit.
#. Go to the
Computer\HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem
key.
#. Edit the value of the LongPathsEnabled property of that key and set
it to 1.
#. Reinstall scikit-learn (ignoring the previous broken installation):
.. prompt:: powershell
pip install --exists-action=i scikit-learn
.. _install_nightly_builds:
The continuous integration servers of the scikit-learn project build, test and upload wheel packages for the most recent Python version on a nightly basis.
You can install the nightly build of scikit-learn using the scientific-python-nightly-wheels
index from the PyPI registry of anaconda.org:
.. prompt:: bash $
pip install --pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple scikit-learn
Note that first uninstalling scikit-learn might be required to be able to install nightly builds of scikit-learn.