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.

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.

Scikit-learn is a Python library to do Machine Learning:

• Simple and efficient tools for data mining and data analysis

• Accessible to everybody, and reusable in various contexts

• Built on NumPy, SciPy, and matplotlib

• Open source, commercially usable - BSD license

added 6 years 2 months ago

Contents related to 'Scikit-learn'

NumPy: NumPy is a library for the Python programming language, adding support for large, along with a large collection of high-level mathematical functions to operate on these arrays.

SciPy: SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering.

Pandas: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

IPython: IPython is a command shell for interactive computing in multiple programming languages, that offers introspection, rich media, shell syntax, tab completion, and history

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