.. _installation-instructions: ======================= Installing scikit-learn ======================= There are different ways to install scikit-learn: * :ref:`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 :ref:`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. * :ref:`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. .. _install_official_release: Installing the latest release ============================= .. This quickstart installation is a hack of the awesome https://spacy.io/usage/#quickstart page. See the original javascript implementation https://github.com/ines/quickstart .. raw:: html
Operating System
Packager
.. raw:: html
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: .. raw:: html
pip3 install -U scikit-learn
pip install -U scikit-learn
pip install -U scikit-learn
python3 -m venv sklearn-venv
  source sklearn-venv/bin/activate
  pip3 install -U scikit-learn
python -m venv sklearn-venv
  sklearn-venv\Scripts\activate
  pip install -U scikit-learn
python -m venv sklearn-venv
  source sklearn-venv/bin/activate
  pip install -U scikit-learn
conda create -n sklearn-env -c conda-forge scikit-learn
  conda activate sklearn-env
In order to check your installation you can use .. raw:: html
python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installed
  python3 -m pip freeze  # to see all packages installed in the active virtualenv
  python3 -c "import sklearn; sklearn.show_versions()"
python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
  python -m pip freeze  # to see all packages installed in the active virtualenv
  python -c "import sklearn; sklearn.show_versions()"
python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
  python -m pip freeze  # to see all packages installed in the active virtualenv
  python -c "import sklearn; sklearn.show_versions()"
python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
  python -m pip freeze  # to see all packages installed in the active virtualenv
  python -c "import sklearn; sklearn.show_versions()"
conda list scikit-learn  # to see which scikit-learn version is installed
  conda list  # to see all packages installed in the active conda environment
  python -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. .. 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 - 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. .. _install_by_distribution: 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: .. prompt:: bash $ 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: .. prompt:: bash $ 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``: .. prompt:: bash $ 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``: .. prompt:: bash $ 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-scikits-learn``, where ``XY`` denotes the Python version. It can be installed by typing the following command: .. prompt:: bash $ 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: .. 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 `_ 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 `__. WinPython for Windows ----------------------- The `WinPython `_ project distributes scikit-learn as an additional plugin. Troubleshooting =============== .. _windows_longpath: 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: #. 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:: bash $ pip install --exists-action=i scikit-learn