|
| website = {{URL|https://scikit-learn.org/}}
}}
'''Scikitscikit-learn''' (formerly '''scikits.learn''' and also known as '''sklearn''') is a [[free software]] [[machine learning]] [[Library (computing)|library]] for the [[Python (programming language)|Python]] [[programming language]].<ref name="jmlr">{{cite journal
|author1=Fabian Pedregosa
|author2=Gaël Varoquaux
|author14=Matthieu Perrot
|author15=Édouard Duchesnay
|title=Scikitscikit-learn: Machine Learning in Python
|journal=Journal of Machine Learning Research
|year=2011
==Overview==
The scikit-learn project started as scikits.learn, a [[Google Summer of Code]] project by French [[data scientist]] [[David Cournapeau]]. ItsThe 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]].<ref>{{cite web
|url=https://scikits.appspot.com/scikit-learn
|title=scikit-learn
|first1=Janto
}}</ref>
The original [[codebase]] was later rewritten by other developers. In 2010, contributors Fabian Pedregosa, GaelGaël Varoquaux, Alexandre Gramfort and Vincent Michel, all from the [[French Institute for Research in Computer Science and Automation]] in [[Plateau de Saclay|Saclay]], [[France]], took leadership of the project and madereleased the first public releaseversion of the library on February the 1st, 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> OfIn theNovember various scikits2012, scikit-learn as well as [[scikit-image]], were described as two of the "well-maintained and popular" {{As of|2012|11|alt=inscikits November 2012libraries}}.<ref>{{cite book
|author=Eli Bressert
|title=SciPy and NumPy: an overview for developers
|url=https://books.google.com/books?id=fLKTuJqQLVEC&pg=PA43
|page=43
}}</ref> ScikitIn 2019, it was noted that scikit-learn is one of the most popular machine learning libraries on [[GitHub]].<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=[[GitHub]]|language=en-US|access-date=2019-10-17}}</ref>
==Implementation==
Scikitscikit-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.
Scikitscikit-learn integrates well with many other Python libraries, such as [[Matplotlib]] and [[plotly]] for plotting, [[NumPy]] for array vectorization, [[Pandas (software)|Pandas]] dataframes, [[SciPy]], and many more.
== Version history ==
Scikitscikit-learn was initially developed by David Cournapeau as a [[Google]] summerSummer of codeCode 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, [[French Institute for Research in Computer Science and Automation|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<ref name=":0" />
* July 2014. scikit-learn 0.15.0<ref name=":0" />
* September 2021. scikit-learn 1.0<ref>{{Citation|title=scikit-learn: A set of python modules for machine learning and data mining|url=http://scikit-learn.org/|access-date=2021-09-24}}</ref>
==Scikitscikit-learn tools==
* [[mlpy]]
* [[SpaCy]]
| 