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In 1984 Apple Computer unveiled their Macintosh. It included a new user interface that revolutionized the way people though about computer interaction. Originally referred to as simply 'Macintosh System', the underlying OS was a single-tasking disk system for the Motorola 68K CPU. Significant changes were made in MacOS 7.x.
Emulation note: For MacOS 0.x-6.x we recommend the vMac Mini emulator.
It is possible to write Macintosh 400k/800k images to a real disk using a Kryoflux.
Note: Wikipedia's/Apple's Developer CD Classic Mac OS 'System Software' numbering scheme is GARBAGE and should NOT be used.
System 1.0 (0.97), Finder 1.0 was the first official, stable release. It was released in 1984 with the original Macintosh and ran with 128k RAM, 400k 3.5' floppy drive, monochrome video, and a mouse. It used a flat file system (MFS) that only emulated folders. Officially the System revision was 1.0, but the internal number reported '0.97'.
The Macintosh Guided Tour was also released with the original Macintosh, but contained an earlier System 0.85, Finder 1.0. Notably the finder still had icons of the Twiggy Macintosh!
System 1.1, Finder 1.1g improved font support, disk copying, and startup speed.
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 System
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).
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In order to check your installation you can use
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Note that in order to avoid potential conflicts with other packages it isstrongly recommended to use a virtual environment (venv) or a conda environment.
Using such an isolated environment makes it possible to install a specificversion of scikit-learn with pip or conda and its dependencies independently ofany previously installed Python packages. In particular under Linux is itdiscouraged to install pip packages alongside the packages managed by thepackage 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).
Scikit-learn plotting capabilities (i.e., functions start with “plot_”and classes end with “Display”) require Matplotlib. The examples requireMatplotlib and some examples require scikit-image, pandas, or seaborn. Theminimum version of Scikit-learn dependencies are listed below along with itspurpose.
benchmark, docs, examples, tests
docs, examples, tests
benchmark, docs, examples, tests
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 and later requires Python 3.7 or newer.
For installing on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+are required.
Installing on Apple Silicon M1 hardware¶
The recently introduced
macos/arm64 platform (sometimes also known as
macos/aarch64) requires the open source community to upgrade the buildconfiguration and automation to properly support it.
At the time of writing (January 2021), the only way to get a workinginstallation of scikit-learn on this hardware is to install scikit-learn and itsdependencies from the conda-forge distribution, for instance using the miniforgeinstallers:
The following issue tracks progress on making it possible to installscikit-learn from PyPI with pip:
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’s package is provided through the official repositories as
python-scikit-learn for Python.It can be installed by typing the following command:
The Debian/Ubuntu package is split in three different packages called
python3-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
The Fedora package is called
python3-scikit-learn for the python 3 version,the only one available in Fedora30.It can be installed using
scikit-learn is available via pkgsrc-wip:
MacPorts for Mac OSX¶
The MacPorts package is named
XY denotes the Python version.It can be installed by typing the followingcommand:
Anaconda and Enthought Deployment Manager for all supported platforms¶
Anaconda andEnthought Deployment Managerboth ship with scikit-learn in addition to a large set of scientificpython library for Windows, Mac OSX and Linux.
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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.
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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.
Error caused by file path length limit on Windows¶
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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 the
AppData folder structure under the user home directory, for instance:
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In this case it is possible to lift that limit in the Windows registry byusing the
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Type “regedit” in the Windows start menu to launch
Go to the
Edit the value of the
LongPathsEnabledproperty of that key and setit to 1.
Reinstall scikit-learn (ignoring the previous broken installation):