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¶
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 (no administrator permission required).
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-envconda activate sklearn-envconda install scikit-learn
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, e.g. python3
(see python3 virtualenv documentation) or conda environments.
Using an isolated environment makes possible to install a specific version of scikit-learn 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).
If you must install scikit-learn and its dependencies with pip, you can install
Scikit-learn plotting capabilities (i.e., functions start with “plot_” and classes end with “Display”) require Matplotlib (>= 2.1.1). For running the examples Matplotlib >= 2.1.1 is required. A few examples require scikit-image >= 0.13, a few examples require pandas >= 0.18.0, some examples require seaborn >= 0.9.0.
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 now requires Python 3.6 or newer.
For installing on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+ are required.
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.
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
The Debian/Ubuntu package is splitted in three different packages called
python3-sklearn (python modules),
implementations and bindings),
Only the Python 3 version is available in the Debian Buster (the more recent
Packages can be installed using
$ sudo apt-get install python3-sklearn python3-sklearn-lib python3-sklearn-doc
The Fedora package is called
python3-scikit-learn for the python 3 version,
the only one available in Fedora30.
It can be installed using
$ sudo dnf install python3-scikit-learn
MacPorts for Mac OSX¶
The MacPorts package is named
XY denotes the Python version.
It can be installed by typing the following
$ sudo port install py36-scikit-learn
Canopy and Anaconda for all supported platforms¶
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.
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
Type “regedit” in the Windows start menu to launch
Go to the
Edit the value of the
LongPathsEnabledproperty of that key and set it to 1.
Reinstall scikit-learn (ignoring the previous broken installation):
pip install --exists-action=i scikit-learn