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

Operating System
Packager
Install the 64bit version of Python 3, for instance from https://www.python.org.Install Python 3 using homebrew (brew install python) or by manually installing the package from https://www.python.org.Install python3 and python3-pip using the package manager of the Linux Distribution.Install conda using the Anaconda or miniconda installers or the miniforge installers (no administrator permission required for any of those).

Then run:

python3 -m venv sklearn-venvpython -m venv sklearn-venvpython -m venv sklearn-venvsource sklearn-venv/bin/activatesource sklearn-venv/bin/activatesklearn-venv\Scripts\activatepip install -U scikit-learnpip install -U scikit-learnpip install -U scikit-learnpip3 install -U scikit-learnconda create -n sklearn-env -c conda-forge scikit-learnconda activate sklearn-env

In order to check your installation you can use

python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installedpython3 -m pip freeze  # to see all packages installed in the active virtualenvpython3 -c "import sklearn; sklearn.show_versions()"python -m pip show scikit-learn  # to see which version and where scikit-learn is installedpython -m pip freeze  # to see all packages installed in the active virtualenvpython -c "import sklearn; sklearn.show_versions()"python -m pip show scikit-learn  # to see which version and where scikit-learn is installedpython -m pip freeze  # to see all packages installed in the active virtualenvpython -c "import sklearn; sklearn.show_versions()"python -m pip show scikit-learn  # to see which version and where scikit-learn is installedpython -m pip freeze  # to see all packages installed in the active virtualenvpython -c "import sklearn; sklearn.show_versions()"conda list scikit-learn  # to see which scikit-learn version is installedconda list  # to see all packages installed in the active conda environmentpython -c "import sklearn; sklearn.show_versions()"

Note that in order to avoid potential conflicts with other packages it is strongly recommended to use a virtual environment (venv) or a conda environment.

Using such an isolated environment 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 is it 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 start with “plot_” and classes end 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

1.17.3

build, install

scipy

1.3.2

build, install

joblib

1.0.0

install

threadpoolctl

2.0.0

install

cython

0.29.24

build

matplotlib

3.1.2

benchmark, docs, examples, tests

scikit-image

0.16.2

docs, examples, tests

pandas

1.0.5

benchmark, docs, examples, tests

seaborn

0.9.0

docs, examples

memory_profiler

0.57.0

benchmark, docs

pytest

5.0.1

tests

pytest-cov

2.9.0

tests

flake8

3.8.2

tests

black

22.3.0

tests

mypy

0.961

tests

pyamg

4.0.0

tests

sphinx

4.0.1

docs

sphinx-gallery

0.7.0

docs

numpydoc

1.2.0

docs, tests

Pillow

7.1.2

docs

sphinx-prompt

1.3.0

docs

sphinxext-opengraph

0.4.2

docs

conda-lock

1.0.5

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 require Python 3.6 or newer. Scikit-learn 1.0 supported Python 3.7-3.10. Scikit-learn 1.1 and later requires Python 3.8 or newer.

Note

For installing on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+ are required.

Installing on Apple Silicon M1 hardware

The recently introduced macos/arm64 platform (sometimes also known as macos/aarch64) requires the open source community to upgrade the build configuration and automation to properly support it.

At the time of writing (January 2021), the only way to get a working installation of scikit-learn on this hardware is to install scikit-learn and its dependencies from the conda-forge distribution, for instance using the miniforge installers:

https://github.com/conda-forge/miniforge

The following issue tracks progress on making it possible to install scikit-learn from PyPI with pip:

https://github.com/scikit-learn/scikit-learn/issues/19137

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). Only the Python 3 version is available in the Debian Buster (the more recent Debian distribution). 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 Fedora30. It can be installed using dnf:

sudo dnf install python3-scikit-learn

NetBSD

scikit-learn is available via pkgsrc-wip:

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 conda channel

Intel maintains a dedicated conda channel that ships scikit-learn:

conda install -c intel scikit-learn

This version of scikit-learn comes with alternative solvers for some common estimators. Those solvers come from the DAAL C++ library and are optimized for multi-core Intel CPUs.

Note that those solvers are not enabled by default, please refer to the daal4py documentation for more details.

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/IntelPython/daal4py.

WinPython for Windows

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

Troubleshooting

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 EnvironmentError: [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