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Installing scikit-learn#

There are different ways to install scikit-learn:

  • Install the latest 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 operating system or Python 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.

  • Building the package from source. This is best for users who want the latest-and-greatest features and aren’t afraid of running brand-new code. This is also needed for users who wish to contribute to the project.

Installing the latest release#

Install the 64-bit version of Python 3, for instance from the official website.

Now create a virtual environment (venv) and install scikit-learn. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages.

python -m venv sklearn-env
sklearn-env\Scripts\activate  # activate
pip install -U scikit-learn

In order to check your installation, you can use:

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()"

Install Python 3 using homebrew (brew install python) or by manually installing the package from the official website.

Now create a virtual environment (venv) and install scikit-learn. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packges.

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:

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()"

Python 3 is usually installed by default on most Linux distributions. To check if you have it installed, try:

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) and install scikit-learn. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages.

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:

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()"

Install conda using the Anaconda or miniconda installers or the miniforge installers (no administrator permission required for any of those). Then run:

conda create -n sklearn-env -c conda-forge scikit-learn
conda activate sklearn-env

In order to check your installation, you can use:

conda list scikit-learn  # show scikit-learn version and location
conda list               # show all installed packages in the environment
python -c "import sklearn; sklearn.show_versions()"

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.

Dependency

Minimum Version

Purpose

numpy

1.19.5

build, install

scipy

1.6.0

build, install

joblib

1.2.0

install

threadpoolctl

3.1.0

install

cython

3.0.10

build

meson-python

0.15.0

build

matplotlib

3.3.4

benchmark, docs, examples, tests

scikit-image

0.17.2

docs, examples, tests

pandas

1.1.5

benchmark, docs, examples, tests

seaborn

0.9.0

docs, examples

memory_profiler

0.57.0

benchmark, docs

pytest

7.1.2

tests

pytest-cov

2.9.0

tests

ruff

0.2.1

tests

black

24.3.0

tests

mypy

1.9

tests

pyamg

4.0.0

tests

polars

0.20.23

docs, tests

pyarrow

12.0.0

tests

sphinx

7.3.7

docs

sphinx-copybutton

0.5.2

docs

sphinx-gallery

0.16.0

docs

numpydoc

1.2.0

docs, tests

Pillow

7.1.2

docs

pooch

1.6.0

docs, examples, tests

sphinx-prompt

1.4.0

docs

sphinxext-opengraph

0.9.1

docs

plotly

5.14.0

docs, examples

sphinxcontrib-sass

0.3.4

docs

sphinx-remove-toctrees

1.0.0.post1

docs

sphinx-design

0.5.0

docs

pydata-sphinx-theme

0.15.2

docs

conda-lock

2.5.6

maintenance

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 0.23-0.24 required Python 3.6 or newer. Scikit-learn 1.0 supported Python 3.7-3.10. Scikit-learn 1.1, 1.2 and 1.3 support Python 3.8-3.12 Scikit-learn 1.4 requires Python 3.9 or newer.

Third party distributions of scikit-learn#

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#

Alpine Linux’s package is provided through the official repositories as py3-scikit-learn for Python. It can be installed by typing the following command:

sudo apk add py3-scikit-learn

Arch Linux#

Arch Linux’s package is provided through the official repositories as python-scikit-learn for Python. It can be installed by typing the following command:

sudo pacman -S python-scikit-learn

Debian/Ubuntu#

The Debian/Ubuntu package is split in three different packages called python3-sklearn (python modules), python3-sklearn-lib (low-level implementations and bindings), python3-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:

sudo apt-get install python3-sklearn python3-sklearn-lib python3-sklearn-doc

Fedora#

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:

sudo dnf install python3-scikit-learn

NetBSD#

scikit-learn is available via pkgsrc-wip: https://pkgsrc.se/math/py-scikit-learn

MacPorts for Mac OSX#

The MacPorts package is named py<XY>-scikits-learn, where XY denotes the Python version. It can be installed by typing the following command:

sudo port install py39-scikit-learn

Anaconda and Enthought Deployment Manager for all supported platforms#

Anaconda and Enthought Deployment Manager 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 Extension for Scikit-learn#

Intel maintains an optimized x86_64 package, available in PyPI (via pip), and in the main, conda-forge and intel conda channels:

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 documentation for more details on usage scenarios. Direct export example:

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 intel/scikit-learn-intelex. If you observe any issue with scikit-learn-intelex, please report the issue on their issue tracker.

WinPython for Windows#

The WinPython project distributes scikit-learn as an additional plugin.

Troubleshooting#

If you encounter unexpected failures when installing scikit-learn, you may submit an issue to the issue tracker. Before that, please also make sure to check the following common issues.

Error caused by file path length limit on Windows#

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:

  1. Type “regedit” in the Windows start menu to launch regedit.

  2. Go to the Computer\HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem key.

  3. Edit the value of the LongPathsEnabled property of that key and set it to 1.

  4. Reinstall scikit-learn (ignoring the previous broken installation):

    pip install --exists-action=i scikit-learn
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