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Installing scikit-learn

There are different ways to get scikit-learn installed:


If you wish to contribute to the project, it’s recommended you install the latest development version.

Installing an official release

Getting the dependencies

Installing from source requires you to have installed Python (>= 2.6), NumPy (>= 1.3), SciPy (>= 0.7), setuptools, Python development headers and a working C++ compiler. Under Debian-based operating systems, which include Ubuntu, you can install all these requirements by issuing:

sudo apt-get install build-essential python-dev python-numpy python-setuptools python-scipy libatlas-dev libatlas3-base


In order to build the documentation and run the example code contains in this documentation you will need matplotlib:

sudo apt-get install python-matplotlib


On older versions of Ubuntu, you might need to apt-get install python-numpy-dev to get the header files for NumPy.

On Ubuntu 10.04 LTS, the package libatlas-dev is called libatlas-headers.


The above installs the ATLAS implementation of BLAS (the Basic Linear Algebra Subprograms library). Ubuntu 11.10 and later, and recent (testing) versions of Debian, offer an alternative implementation called OpenBLAS.

Using OpenBLAS can give speedups in some scikit-learn modules, but it doesn’t play nicely with joblib/multiprocessing, so using it is not recommended unless you know what you’re doing.

If you do want to use OpenBLAS, then replacing ATLAS only requires two commands. ATLAS has to be removed, otherwise NumPy may not work:

sudo apt-get remove libatlas3gf-base libatlas-dev
sudo apt-get install libopenblas-dev

On Red Hat and clones (e.g. CentOS), install the dependencies using:

sudo yum -y install gcc gcc-c++ numpy python-devel scipy

Easy install

This is usually the fastest way to install the latest stable release. If you have pip or easy_install, you can install or update with the command:

pip install -U scikit-learn


easy_install -U scikit-learn

for easy_install. Note that you might need root privileges to run these commands.

From source package

Download the package from , unpack the sources and cd into the source directory.

This packages uses distutils, which is the default way of installing python modules. The install command is:

python install

Windows installer

You can download a Windows installer from downloads in the project’s web page. Note that must also have installed the packages numpy and setuptools.

This package is also expected to work with python(x,y) as of

Installing on Windows 64-bit

To install a 64-bit version of scikit-learn, you can download the binaries from Note that this will require a compatible version of numpy, scipy and matplotlib. The easiest option is to also download them from the same URL.

Building on windows

To build scikit-learn on windows you will need a C/C++ compiler in addition to numpy, scipy and setuptools. At least MinGW (a port of GCC to Windows OS) and Microsoft Visual C++ 2008 should work out of the box. To force the use of a particular compiler, write a file named setup.cfg in the source directory with the content:



where my_compiler should be one of mingw32 or msvc.

When the appropriate compiler has been set, and assuming Python is in your PATH (see Python FAQ for windows for more details), installation is done by executing the command:

python install

To build a precompiled package like the ones distributed at the downloads section, the command to execute is:

python bdist_wininst -b doc/logos/scikit-learn-logo.bmp

This will create an installable binary under directory dist/.

Third party distributions of scikit-learn

Some third-party distributions are now providing 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 Python and OS distributions that provide their own version of scikit-learn.

Debian and derivatives (Ubuntu)

The Debian package is named python-sklearn (formerly python-scikits-learn) and can be installed using the following command:

sudo apt-get install python-sklearn

Additionally, backport builds of the most recent release of scikit-learn for existing releases of Debian and Ubuntu are available from the NeuroDebian repository .

A quick-‘n’-dirty way of rolling your own .deb package is to use stdeb.


The Python(x,y) distributes scikit-learn as an additional plugin, which can be found in the Additional plugins page.

Enthought Python distribution

The Enthought Python Distribution already ships a recent version.


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 py26-scikit-learn


sudo port install py27-scikit-learn


Archlinux’s package is provided at Arch User Repository (AUR) with name python2-scikit-learn for latest stable version and python2-scikit-learn-git for building from git version. If yaourt is available, it can be installed by typing the following command:

sudo yaourt -S python2-scikit-learn


sudo yaourt -S python2-scikit-learn-git

depending on the version of scikit-learn you want to use.


scikit-learn is available via pkgsrc-wip:

Bleeding Edge

See section Retrieving the latest code on how to get the development version.


Testing requires having the nose library. After installation, the package can be tested by executing from outside the source directory:

nosetests sklearn --exe

This should give you a lot of output (and some warnings) but eventually should finish with a message similar to:

Ran 601 tests in 27.920s

Otherwise, please consider posting an issue into the bug tracker or to the Mailing List.


Alternative testing method

If for some reason the recommended method is failing for you, please try the alternate method:

python -c "import sklearn; sklearn.test()"

This method might display doctest failures because of nosetests issues.

scikit-learn can also be tested without having the package installed. For this you must compile the sources inplace from the source directory:

python build_ext --inplace

Test can now be run using nosetests:

nosetests sklearn/

This is automated by the commands:

make in


make test