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{{ |
{{Short description|Python library for machine learning}} | ||
{{lowercase title}} | {{lowercase title}} | ||
{{Infobox software | {{Infobox software | ||
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| latest release version = {{wikidata|property|reference|P348}} | | latest release version = {{wikidata|property|reference|P348}} | ||
| latest release date = {{start date and age|{{wikidata|qualifier|P348|P577}}}} | | latest release date = {{start date and age|{{wikidata|qualifier|P348|P577}}}} | ||
| latest preview version = |
| latest preview version = | ||
| latest preview date = | | latest preview date = | ||
| programming language = ], ], ] and ] |
| programming language = ], ], ] and ]{{wikidata|reference|P277}} | ||
| operating system = ], ], ] | | operating system = ], ], ] | ||
| platform = | | platform = | ||
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| website = {{URL|https://scikit-learn.org/}} | | website = {{URL|https://scikit-learn.org/}} | ||
}} | }} | ||
''' |
'''scikit-learn''' (formerly '''scikits.learn''' and also known as '''sklearn''') is a ] ] ] for the ] ].<ref name="jmlr">{{cite journal | ||
|author1=Fabian Pedregosa | |author1=Fabian Pedregosa | ||
|author2=Gaël Varoquaux | |author2=Gaël Varoquaux | ||
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|author14=Matthieu Perrot | |author14=Matthieu Perrot | ||
|author15=Édouard Duchesnay | |author15=Édouard Duchesnay | ||
|title= |
|title=scikit-learn: Machine Learning in Python | ||
|journal=Journal of Machine Learning Research | |journal=Journal of Machine Learning Research | ||
|year=2011 | |year=2011 | ||
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|url=http://jmlr.org/papers/v12/pedregosa11a.html | |url=http://jmlr.org/papers/v12/pedregosa11a.html | ||
}}</ref> | }}</ref> | ||
It features various ], ] and ] algorithms including ]s, ], ], ] and ], and is designed to interoperate with the Python numerical and scientific libraries ] and ]. | It features various ], ] and ] ] including ]s, ], ], ] and ], and is designed to interoperate with the ] numerical and scientific libraries ] and ]. Scikit-learn is a ] fiscally sponsored project.<ref>{{cite web|title=NumFOCUS Sponsored Projects|url=https://numfocus.org/sponsored-projects|publisher=NumFOCUS|access-date=2021-10-25}}</ref> | ||
==Overview== | ==Overview== | ||
The scikit-learn project started as scikits.learn, a ] project by ]. |
The scikit-learn project started as scikits.learn, a ] project by French ] ]. The name of the project stems from the notion that it is a "SciKit" (SciPy Toolkit), a separately developed and distributed third-party extension to ].<ref>{{cite web | ||
|url=https://scikits.appspot.com/scikit-learn | |url=https://scikits.appspot.com/scikit-learn | ||
|title=scikit-learn | |title=scikit-learn | ||
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|first1=Janto | |first1=Janto | ||
}}</ref> | }}</ref> | ||
The original ] was later rewritten by other developers. In 2010 Fabian Pedregosa, |
The original ] was later rewritten by other ]. In 2010, contributors Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort and Vincent Michel, from the ] in ], ], took leadership of the project and released the first public version of the library on February 1, 2010.<ref>{{cite web|url=https://scikit-learn.org/stable/about.html#history|title=About us — scikit-learn 0.20.1 documentation|website=scikit-learn.org}}</ref> In November 2012, scikit-learn as well as ] were described as two of the "well-maintained and popular" {{As of|2012|11|alt=scikits libraries}}.<ref>{{cite book | ||
|author=Eli Bressert | |author=Eli Bressert | ||
|title=SciPy and NumPy: an overview for developers | |title=SciPy and NumPy: an overview for developers | ||
Line 62: | Line 62: | ||
|url=https://books.google.com/books?id=fLKTuJqQLVEC&pg=PA43 | |url=https://books.google.com/books?id=fLKTuJqQLVEC&pg=PA43 | ||
|page=43 | |page=43 | ||
}}</ref> |
}}</ref> In 2019, it was noted that scikit-learn is one of the most popular machine learning libraries on ].<ref>{{Cite web|url=https://github.blog/2019-01-24-the-state-of-the-octoverse-machine-learning/|title=The State of the Octoverse: machine learning|date=2019-01-24|website=The GitHub Blog|publisher=]|language=en-US|access-date=2019-10-17}}</ref> | ||
==Implementation== | ==Implementation== | ||
scikit-learn is largely written in Python, and uses ] extensively for high-performance linear algebra and array operations. Furthermore, some core algorithms are written in ] to improve performance. Support vector machines are implemented by a Cython wrapper around ]; logistic regression and linear support vector machines by a similar wrapper around ]. In such cases, extending these methods with Python may not be possible. | |||
scikit-learn integrates well with many other Python libraries, such as ] and ] for plotting, ] for array vectorization, ] dataframes, ], and many more. | |||
== Version history == | == Version history == | ||
scikit-learn was initially developed by David Cournapeau as a Google Summer of Code project in 2007. Later that year, Matthieu Brucher joined the project and started to use it as a part of his thesis work. In 2010, ], the ], got involved and the first public release (v0.1 beta) was published in late January 2010. | |||
⚫ | * August 2013. scikit-learn 0.14<ref name=":0" /> | ||
⚫ | * July 2014. scikit-learn 0.15.0<ref name=":0" /> | ||
⚫ | * March 2015. scikit-learn 0.16.0<ref name=":0" /> | ||
* November 2015. scikit-learn 0.17.0<ref name=":0">{{Cite web|url=https://scikit-learn.org/dev/whats_new.html|title=Release history — scikit-learn 0.19.dev0 documentation|website=scikit-learn.org|access-date=2017-02-27}}</ref> | |||
⚫ | * September 2016. scikit-learn 0.18.0 | ||
⚫ | * July 2017. scikit-learn 0.19.0 | ||
* September 2018. scikit-learn 0.20.0<ref>{{cite web |title=Release History - 0.20.0 documentation |url=https://scikit-learn.org/stable/whats_new.html#version-0-20 |website=scikit-learn |access-date=6 November 2018}}</ref> | |||
* May 2019. scikit-learn 0.21.0<ref>{{cite web |title=Release History - 0.21.0 documentation |url=https://scikit-learn.org/stable/whats_new.html#version-0-21-0 |website=scikit-learn |access-date=5 May 2019}}</ref> | |||
* December 2019. scikit-learn 0.22<ref>{{cite web |title=Release History - 0.22 documentation |url=https://scikit-learn.org/dev/whats_new/v0.22.html |website=scikit-learn |access-date=7 June 2020}}</ref> | |||
* May 2020. scikit-learn 0.23.0<ref>{{cite web |title=Release History - 0.23.0 documentation |url=https://scikit-learn.org/dev/whats_new/v0.23.html#version-0-23-0 |website=scikit-learn |access-date=7 June 2020}}</ref> | * May 2020. scikit-learn 0.23.0<ref>{{cite web |title=Release History - 0.23.0 documentation |url=https://scikit-learn.org/dev/whats_new/v0.23.html#version-0-23-0 |website=scikit-learn |access-date=7 June 2020}}</ref> | ||
* |
* Jan 2021. scikit-learn 0.24<ref>{{Citation|title=Release History - 0.24 documentation |url=https://scikit-learn.org/dev/whats_new/v0.24.html |website=scikit-learn |access-date=2021-02-08}}</ref> | ||
* |
* September 2021. scikit-learn 1.0.0<ref>{{cite web |title=Release History - 1.0.0 documentation |url=https://scikit-learn.org/dev/whats_new/v1.0.html#version-1-0-0 |website=scikit-learn }}</ref> | ||
* |
* October 2021. scikit-learn 1.0.1<ref>{{cite web |title=Release History - 1.0.1 documentation |url=https://scikit-learn.org/dev/whats_new/v1.0.html#version-1-0-1 |website=scikit-learn }}</ref> | ||
* December 2021. scikit-learn 1.0.2<ref>{{cite web |title=Release History - 1.0.2 documentation |url=https://scikit-learn.org/dev/whats_new/v1.0.html |website=scikit-learn }}</ref> | |||
⚫ | * July 2017. scikit-learn 0.19.0 | ||
* May 2022. scikit-learn 1.1.0<ref>{{cite web |title=Release History - 1.1.0 documentation |url=https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-0 |website=scikit-learn }}</ref> | |||
⚫ | * September 2016. scikit-learn 0.18.0 |
||
* |
* May 2022. scikit-learn 1.1.1<ref>{{cite web |title=Release History - 1.1.1 documentation |url=https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-1 |website=scikit-learn }}</ref> | ||
* August 2022. scikit-learn 1.1.2<ref>{{cite web |title=Release History - 1.1.2 documentation |url=https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-2 |website=scikit-learn }}</ref> | |||
⚫ | * March 2015. scikit-learn 0.16.0<ref name=":0" /> |
||
* October 2022. scikit-learn 1.1.3<ref>{{cite web |title=Release History - 1.1.3 documentation |url=https://scikit-learn.org/dev/whats_new/v1.1.html |website=scikit-learn }}</ref> | |||
⚫ | * July 2014. scikit-learn 0.15.0<ref name=":0" /> | ||
* December 2022. scikit-learn 1.2.0<ref>{{cite web |title=Release History - 1.2.0 documentation |url=https://scikit-learn.org/dev/whats_new/v1.2.html#version-1-2-0 |website=scikit-learn }}</ref> | |||
⚫ | * August 2013. scikit-learn 0.14<ref name=":0" /> | ||
* January 2023. scikit-learn 1.2.1<ref>{{cite web |title=Release History - 1.2.1 documentation |url=https://scikit-learn.org/dev/whats_new/v1.2.html#version-1-2-1 |website=scikit-learn }}</ref> | |||
* March 2023. scikit-learn 1.2.2<ref>{{cite web |title=Release History - 1.2.2 documentation |url=https://scikit-learn.org/dev/whats_new/v1.2.html |website=scikit-learn }}</ref> | |||
The timeline of releases is depicted in the following chart. | |||
{{#tag:timeline| | |||
ImageSize = width:700 height:auto barincrement:26 | |||
PlotArea = left:130 bottom:100 top:10 right:20 | |||
Alignbars = justify | |||
DateFormat = dd/mm/yyyy | |||
Period = from:01/01/2013 till:31/12/2021 | |||
TimeAxis = orientation:horizontal format:yyyy | |||
Legend = orientation:vertical position:bottom columns:1 | |||
ScaleMajor = increment:2 start:2014 | |||
ScaleMinor = increment:1 start:2013 | |||
Colors = | |||
id:p1 value:red legend:VersionRelease | |||
BarData = | |||
bar:0.23.0 text:scikit-learn 0.23.0 | |||
bar:0.22.0 text:scikit-learn 0.22.0 | |||
bar:0.21.0 text:scikit-learn 0.21.0 | |||
bar:0.20.0 text:scikit-learn 0.20.0 | |||
bar:0.19.0 text:scikit-learn 0.19.0 | |||
bar:0.18.0 text:scikit-learn 0.18.0 | |||
bar:0.17.0 text:scikit-learn 0.17.0 | |||
bar:0.16.0 text:scikit-learn 0.16.0 | |||
bar:0.15.0 text:scikit-learn 0.15.0 | |||
bar:0.14.0 text:scikit-learn 0.14.0 | |||
PlotData= | |||
width:11 textcolor:black align:left anchor:from shift:(11,-4) | |||
color:p1 | |||
bar:0.23.0 from:01/05/2020 till:30/05/2020 width:3 | |||
bar:0.22.0 from:01/12/2019 till:30/12/2019 width:3 | |||
bar:0.21.0 from:01/05/2019 till:30/05/2019 width:3 | |||
bar:0.20.0 from:01/09/2018 till:30/09/2018 width:3 | |||
bar:0.19.0 from:01/07/2017 till:30/07/2017 width:3 | |||
bar:0.18.0 from:01/09/2016 till:30/09/2016 width:3 | |||
bar:0.17.0 from:01/11/2015 till:30/11/2015 width:3 | |||
bar:0.16.0 from:01/03/2015 till:30/03/2015 width:3 | |||
bar:0.15.0 from:01/07/2014 till:30/07/2014 width:3 | |||
bar:0.14.0 from:01/08/2013 till:30/08/2013 width:3 | |||
}} | |||
==scikit-learn alternatives== | |||
==See also== | |||
* ] | * ] | ||
* ] | * ] | ||
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==External links== | ==External links== | ||
* {{Official website|https://scikit-learn.org/}} | * {{Official website|https://scikit-learn.org/}} | ||
* {{GitHub|https://github.com/scikit-learn}} | |||
{{SciPy ecosystem}} | {{SciPy ecosystem}} | ||
{{differentiable computing}} | |||
] | ] |
Latest revision as of 21:08, 25 November 2024
Python library for machine learningOriginal author(s) | David Cournapeau |
---|---|
Initial release | June 2007; 17 years ago (2007-06) |
Stable release | 1.6.0 / 9 December 2024; 23 days ago (9 December 2024) |
Repository | |
Written in | Python, Cython, C and C++ |
Operating system | Linux, macOS, Windows |
Type | Library for machine learning |
License | New BSD License |
Website | scikit-learn |
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project.
Overview
The scikit-learn project started as scikits.learn, a Google Summer of Code project by French data scientist David Cournapeau. The name of the project stems from the notion that it is a "SciKit" (SciPy Toolkit), a separately developed and distributed third-party extension to SciPy. The original codebase was later rewritten by other developers. In 2010, contributors Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort and Vincent Michel, from the French Institute for Research in Computer Science and Automation in Saclay, France, took leadership of the project and released the first public version of the library on February 1, 2010. In November 2012, scikit-learn as well as scikit-image were described as two of the "well-maintained and popular" scikits libraries. In 2019, it was noted that scikit-learn is one of the most popular machine learning libraries on GitHub.
Implementation
scikit-learn is largely written in Python, and uses NumPy extensively for high-performance linear algebra and array operations. Furthermore, some core algorithms are written in Cython to improve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM; logistic regression and linear support vector machines by a similar wrapper around LIBLINEAR. In such cases, extending these methods with Python may not be possible.
scikit-learn integrates well with many other Python libraries, such as Matplotlib and plotly for plotting, NumPy for array vectorization, Pandas dataframes, SciPy, and many more.
Version history
scikit-learn was initially developed by David Cournapeau as a Google Summer of Code project in 2007. Later that year, Matthieu Brucher joined the project and started to use it as a part of his thesis work. In 2010, INRIA, the French Institute for Research in Computer Science and Automation, got involved and the first public release (v0.1 beta) was published in late January 2010.
- August 2013. scikit-learn 0.14
- July 2014. scikit-learn 0.15.0
- March 2015. scikit-learn 0.16.0
- November 2015. scikit-learn 0.17.0
- September 2016. scikit-learn 0.18.0
- July 2017. scikit-learn 0.19.0
- September 2018. scikit-learn 0.20.0
- May 2019. scikit-learn 0.21.0
- December 2019. scikit-learn 0.22
- May 2020. scikit-learn 0.23.0
- Jan 2021. scikit-learn 0.24
- September 2021. scikit-learn 1.0.0
- October 2021. scikit-learn 1.0.1
- December 2021. scikit-learn 1.0.2
- May 2022. scikit-learn 1.1.0
- May 2022. scikit-learn 1.1.1
- August 2022. scikit-learn 1.1.2
- October 2022. scikit-learn 1.1.3
- December 2022. scikit-learn 1.2.0
- January 2023. scikit-learn 1.2.1
- March 2023. scikit-learn 1.2.2
scikit-learn alternatives
References
- "Release 1.6.0". 9 December 2024. Retrieved 29 December 2024.
- "The scikit-learn Open Source Project on Open Hub: Languages Page". Open Hub. Retrieved 14 July 2018.
- Fabian Pedregosa; Gaël Varoquaux; Alexandre Gramfort; Vincent Michel; Bertrand Thirion; Olivier Grisel; Mathieu Blondel; Peter Prettenhofer; Ron Weiss; Vincent Dubourg; Jake Vanderplas; Alexandre Passos; David Cournapeau; Matthieu Perrot; Édouard Duchesnay (2011). "scikit-learn: Machine Learning in Python". Journal of Machine Learning Research. 12: 2825–2830.
- "NumFOCUS Sponsored Projects". NumFOCUS. Retrieved 2021-10-25.
- Dreijer, Janto. "scikit-learn".
- "About us — scikit-learn 0.20.1 documentation". scikit-learn.org.
- Eli Bressert (2012). SciPy and NumPy: an overview for developers. O'Reilly. p. 43.
- "The State of the Octoverse: machine learning". The GitHub Blog. GitHub. 2019-01-24. Retrieved 2019-10-17.
- ^ "Release history — scikit-learn 0.19.dev0 documentation". scikit-learn.org. Retrieved 2017-02-27.
- "Release History - 0.20.0 documentation". scikit-learn. Retrieved 6 November 2018.
- "Release History - 0.21.0 documentation". scikit-learn. Retrieved 5 May 2019.
- "Release History - 0.22 documentation". scikit-learn. Retrieved 7 June 2020.
- "Release History - 0.23.0 documentation". scikit-learn. Retrieved 7 June 2020.
- "Release History - 0.24 documentation", scikit-learn, retrieved 2021-02-08
- "Release History - 1.0.0 documentation". scikit-learn.
- "Release History - 1.0.1 documentation". scikit-learn.
- "Release History - 1.0.2 documentation". scikit-learn.
- "Release History - 1.1.0 documentation". scikit-learn.
- "Release History - 1.1.1 documentation". scikit-learn.
- "Release History - 1.1.2 documentation". scikit-learn.
- "Release History - 1.1.3 documentation". scikit-learn.
- "Release History - 1.2.0 documentation". scikit-learn.
- "Release History - 1.2.1 documentation". scikit-learn.
- "Release History - 1.2.2 documentation". scikit-learn.
External links
Scientific software in Python | |
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Differentiable computing | |
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General | |
Hardware | |
Software libraries | |