• Uninstall Anaconda Python Mac
• Anaconda Python Program
• Python Anaconda Download

There are different ways to install scikit-learn:

• Install the latest official release. Thisis the best approach for most users. It will provide a stable versionand pre-built packages are available for most platforms.

• Install the version of scikit-learn provided by youroperating system or Python distribution.This is a quick option for those who have operating systems or Pythondistributions that distribute scikit-learn.It might not provide the latest release version.

• Building the package from source. This is best for users who want thelatest-and-greatest features and aren’t afraid of runningbrand-new code. This is also needed for users who wish to contribute to theproject.

Installing the latest release¶

Operating SystemWindows

Download the graphical macOS installer for your version of Python. RECOMMENDED: Verify data integrity with SHA-256. For more information on hashes, see What about cryptographic hash verification? Double-click the downloaded file and click continue to start the installation. Answer the prompts on the Introduction, Read Me, and License screens. Where packages, notebooks, projects and environments are shared. Your place for free public conda package hosting. Download the Anaconda macOS Python Package. Open the.Anaconda3-2019.10-MacOSX-x8664.pkg. installer from the Downloads folder (or where ever you have it saved). Click Continue to allow the installer to determine the software compatibility. Click Continue to proceed through the Introduction, Read Me and Licence. Make sure you accept the Licence! The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media. The easy way to get up and running with Spyder on any of our supported platforms is to download it as part of the Anaconda distribution, and use the conda package and environment manager to keep it and your other packages installed and up to date. We recommend the latest 64-bit Python 3 version, unless you have specific requirements that dictate otherwise.

macOSLinux
Packagerpipconda

Then run:

In order to check your installation you can use

Note that in order to avoid potential conflicts with other packages it isstrongly recommended to use a virtual environment, e.g. python3 virtualenv(see python3 virtualenv documentation) or conda environments.

Using an isolated environment makes possible to install a specific version ofscikit-learn and its dependencies independently of any previously installedPython packages.In particular under Linux is it discouraged to install pip packages alongsidethe packages managed by the package manager of the distribution(apt, dnf, pacman…).

Note that you should always remember to activate the environment of your choiceprior 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 usingconda 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 usingparticular configurations of operating system and hardware (such as Linux ona Raspberry Pi).

If you must install scikit-learn and its dependencies with pip, you can installit as scikit-learn[alldeps].

Scikit-learn plotting capabilities (i.e., functions start with “plot_”and classes end with “Display”) require Matplotlib (>= 2.1.1). For running theexamples Matplotlib >= 2.1.1 is required. A few examples requirescikit-image >= 0.13, a few examples require pandas >= 0.18.0, some examplesrequire seaborn >= 0.9.0.

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 now requires Python 3.6 or newer.

Note

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 ofscikit-learn integrated with their package-management systems.

These can make installation and upgrading much easier for users sincethe integration includes the ability to automatically installdependencies (numpy, scipy) that scikit-learn requires.

The following is an incomplete list of OS and python distributionsthat provide their own version of scikit-learn.

Arch Linux¶

Arch Linux’s package is provided through the official repositories aspython-scikit-learn for Python.It can be installed by typing the following command:

Debian/Ubuntu¶

The Debian/Ubuntu package is splitted in three different packages calledpython3-sklearn (python modules), python3-sklearn-lib (low-levelimplementations and bindings), python3-sklearn-doc (documentation).Only the Python 3 version is available in the Debian Buster (the more recentDebian distribution).Packages can be installed using apt-get:

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:

Uninstall Anaconda Python Mac

NetBSD¶

scikit-learn is available via pkgsrc-wip:

MacPorts for Mac OSX¶

Anaconda Python Program

The MacPorts package is named py<XY>-scikits-learn,where XY denotes the Python version.It can be installed by typing the followingcommand:

Canopy and Anaconda for all supported platforms¶

Canopy and Anaconda both ship a recentversion of scikit-learn, in addition to a large set of scientific pythonlibrary 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:

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

Note that those solvers are not enabled by default, please refer to thedaal4py documentationfor more details.

Compatibility with the standard scikit-learn solvers is checked by running thefull scikit-learn test suite via automated continuous integration as reportedon https://github.com/IntelPython/daal4py.

WinPython for Windows¶

The WinPython project distributesscikit-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 pathsize limit of Windows if Python is installed in a nested location such as theAppData folder structure under the user home directory, for instance:

In this case it is possible to lift that limit in the Windows registry byusing the regedit tool:

1. Type “regedit” in the Windows start menu to launch regedit.

2. Go to theComputerHKEY_LOCAL_MACHINESYSTEMCurrentControlSetControlFileSystemkey.

3. Edit the value of the LongPathsEnabled property of that key and setit to 1.

4. Reinstall scikit-learn (ignoring the previous broken installation):

Getting started with JupyterLab

The installation guide contains more detailed instructions

Install with conda

Python Anaconda Download

If you use conda, you can install it with:

Install with pip

If you use pip, you can install it with:

If installing using pip install --user, you must add the user-level bin directory to your PATH environment variable in order to launch jupyter lab. If you are using a Unix derivative (FreeBSD, GNU / Linux, OS X), you can achieve this by using export PATH='$HOME/.local/bin:$PATH' command.

Run JupyterLab

Once installed, launch JupyterLab with:

Getting started with the classic Jupyter Notebook

conda

We recommend installing the classic Jupyter Notebook using the conda package manager. Either the miniconda or the miniforge conda distributions include a minimal conda installation.

Then you can install the notebook with:

pip

If you use pip, you can install it with:

Congratulations, you have installed Jupyter Notebook! To run the notebook, run the following command at the Terminal (Mac/Linux) or Command Prompt (Windows):

See Running the Notebook for more details.

Getting started with Voilà

Installation

Voilà can be installed using conda or pip. For more detailed instructions, consult the installation guide.

conda

If you use conda, you can install it with:

pip

If you use pip, you can install it with: