Scikit-learn

Last updated
scikit-learn
Original author(s) David Cournapeau
Initial releaseJune 2007;17 years ago (2007-06)
Stable release
1.5.0 [1] / 21 May 2024;2 months ago (21 May 2024)
Repository
Written in Python, Cython, C and C++ [2]
Operating system Linux, macOS, Windows
Type Library for machine learning
License New BSD License
Website scikit-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]

Contents

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. [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

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.

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References

  1. ↑ "Release 1.5.0". 21 May 2024. Retrieved 25 May 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. 1 2 3 4 "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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