Installing the latest release ¶

Then run:

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

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.

Dependency

Minimum Version

Purpose

numpy

1.19.5

build, install

scipy

1.6.0

build, install

joblib

1.2.0

install

threadpoolctl

2.0.0

install

cython

3.0.8

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

0.0.272

tests

black

23.3.0

tests

tests

pyamg

4.0.0

tests

polars

0.19.12

tests

pyarrow

12.0.0

tests

sphinx

6.0.0

sphinx-copybutton

0.5.2

sphinx-gallery

0.15.0

numpydoc

1.2.0

docs, tests

Pillow

7.1.2

pooch

1.6.0

docs, examples, tests

sphinx-prompt

1.3.0

sphinxext-opengraph

0.4.2

plotly

5.14.0

docs, examples

conda-lock

2.4.2

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.

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 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¶
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
Edit the value of the LongPathsEnabled property of that key and set
it to 1.
Reinstall scikit-learn (ignoring the previous broken installation):
pip install --exists-action=i scikit-learn