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scikit-learn

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scikit-learn
Original author(s)David Cournapeau
Initial releaseJune 2007; 17 years ago (2007-06)
Stable release
1.5.2[1] / 11 September 2024; 59 days ago (11 September 2024)
Repository
Written inPython, Cython, C and C++[2]
Operating systemLinux, macOS, Windows
TypeLibrary for machine learning
LicenseNew BSD License
Websitescikit-learn.org

scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language.[3] 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.[4]

Overview

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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.[5] 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.[6] In November 2012, scikit-learn as well as scikit-image were described as two of the "well-maintained and popular" scikits libraries.[7] In 2019, it was noted that scikit-learn is one of the most popular machine learning libraries on GitHub.[8]

Implementation

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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

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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[9]
  • July 2014. scikit-learn 0.15.0[9]
  • March 2015. scikit-learn 0.16.0[9]
  • November 2015. scikit-learn 0.17.0[9]
  • September 2016. scikit-learn 0.18.0
  • July 2017. scikit-learn 0.19.0
  • September 2018. scikit-learn 0.20.0[10]
  • May 2019. scikit-learn 0.21.0[11]
  • December 2019. scikit-learn 0.22[12]
  • May 2020. scikit-learn 0.23.0[13]
  • Jan 2021. scikit-learn 0.24[14]
  • September 2021. scikit-learn 1.0.0[15]
  • October 2021. scikit-learn 1.0.1[16]
  • December 2021. scikit-learn 1.0.2[17]
  • May 2022. scikit-learn 1.1.0[18]
  • May 2022. scikit-learn 1.1.1[19]
  • August 2022. scikit-learn 1.1.2[20]
  • October 2022. scikit-learn 1.1.3[21]
  • December 2022. scikit-learn 1.2.0[22]
  • January 2023. scikit-learn 1.2.1[23]
  • March 2023. scikit-learn 1.2.2[24]

scikit-learn alternatives

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References

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  1. ^ "Release 1.5.2". 11 September 2024. Retrieved 26 September 2024.
  2. ^ "The scikit-learn Open Source Project on Open Hub: Languages Page". Open Hub. Retrieved 14 July 2018.
  3. ^ 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.
  4. ^ "NumFOCUS Sponsored Projects". NumFOCUS. Retrieved 2021-10-25.
  5. ^ Dreijer, Janto. "scikit-learn".
  6. ^ "About us — scikit-learn 0.20.1 documentation". scikit-learn.org.
  7. ^ Eli Bressert (2012). SciPy and NumPy: an overview for developers. O'Reilly. p. 43.
  8. ^ "The State of the Octoverse: machine learning". The GitHub Blog. GitHub. 2019-01-24. Retrieved 2019-10-17.
  9. ^ a b c d "Release history — scikit-learn 0.19.dev0 documentation". scikit-learn.org. Retrieved 2017-02-27.
  10. ^ "Release History - 0.20.0 documentation". scikit-learn. Retrieved 6 November 2018.
  11. ^ "Release History - 0.21.0 documentation". scikit-learn. Retrieved 5 May 2019.
  12. ^ "Release History - 0.22 documentation". scikit-learn. Retrieved 7 June 2020.
  13. ^ "Release History - 0.23.0 documentation". scikit-learn. Retrieved 7 June 2020.
  14. ^ "Release History - 0.24 documentation", scikit-learn, retrieved 2021-02-08
  15. ^ "Release History - 1.0.0 documentation". scikit-learn.
  16. ^ "Release History - 1.0.1 documentation". scikit-learn.
  17. ^ "Release History - 1.0.2 documentation". scikit-learn.
  18. ^ "Release History - 1.1.0 documentation". scikit-learn.
  19. ^ "Release History - 1.1.1 documentation". scikit-learn.
  20. ^ "Release History - 1.1.2 documentation". scikit-learn.
  21. ^ "Release History - 1.1.3 documentation". scikit-learn.
  22. ^ "Release History - 1.2.0 documentation". scikit-learn.
  23. ^ "Release History - 1.2.1 documentation". scikit-learn.
  24. ^ "Release History - 1.2.2 documentation". scikit-learn.
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