Scikit-learn

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Scikit-learn (formerly scikits.learn) is a free software 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.

Video Scikit-learn

Overview

The scikit-learn project started as scikits.learn, a Google Summer of Code project by David Cournapeau. Its name 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 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel, all from INRIA took leadership of the project and made the first public release on February the 1st 2010. Of the various scikits, scikit-learn as well as scikit-image were described as "well-maintained and popular" in November 2012.

As of 2018, scikit-learn is under active development.

Maps Scikit-learn

Implementation

Scikit-learn is largely written in Python, with some core algorithms written in Cython to achieve 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.

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Version History

Scikit-learn was initially developed by David Cournapeau as a Google summer of code project in 2007. Later 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.

• July 2017. scikit-learn 0.19.0
• September 2016. scikit-learn 0.18.0
• November 2015. scikit-learn 0.17.0
• March 2015. scikit-learn 0.16.0
• July 2014. scikit-learn 0.15.0
• August 2013. scikit-learn 0.14

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See also

• mlpy
• NLTK
• Orange
• TensorFlow
• List of numerical analysis software
• Numpy
• SciPy
• matplotlib
• Pandas

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References

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External links

• Official website
• scikit-learn on GitHub
• Introduction to Machine Learning with Python. Book based in Scikit-learn

Source of article : Wikipedia