scikit-learn – Machine Learning in Python

scikit-learn is an open source Python module for machine learning built on top of SciPy. It offers efficient versions of a large number of common algorithms. The software displays a clean, uniform, and streamlined API, with good online documentation.

The software provides 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 largely written in Python, with some core algorithms written in Cython to optimise performance.

scikit-learn is one of the most useful modules for machine learning in Python.

The software has the following dependencies:

  • Python (>= 2.7 or >= 3.4)
  • NumPy (>= 1.8.2)
  • SciPy (>= 0.13.3)

scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra Subprograms library, and matplotlib to generate the supplied examples.

Features include:

  • Simple and efficient tools for data mining and data analysis.
  • Supervised learning algorithms – great coverage for this type of algorithm. There’s generalised Linear Modules, Linear and Quadratic Discriminant Analysis, Support Vector Machines, Decision Trees, Bayesian methods, Gaussian Processes, Neural network models, and more.
  • Cross-validation – various methods to check the accuracy of supervised models on unseen data.
  • Unsupervised learning algorithms – again, a wide range of algorithms are available, including Gaussian mixture modules, manifold learning, clustering, factor analysis, covariance estimation, density estimation, and more.
  • Dataset transformations – provides a library of transformers, which clean, reduce, expand, or generate feature representations.
  • Feature extraction – useful for extracting features from images and text.
  • Various sample datasets – the datasets are useful in learning how to use scikit-learn. They include: Boston house prices dataset, iris dataset, diabetes dataset, digits dataset, linnerud dataset, wine dataset, and a breast cancer dataset.
  • Built on NumPy, SciPy, and matplotlib.

Website: scikit-learn.org
Support: Documentation, GitHub, Gitter, Mailing List
Developer: scikit team
License: New BSD License

scikit-learn

scikit-learn is written in Python. Learn Python with our recommended free books and free tutorials.

Return to Essential Python Tools Home Page


Popular series
Guide to LinuxNew to Linux? Read our Linux for Starters series. We start right at the basics and teach you everything you need to know to get started with Linux.
Free and Open Source SoftwareThe largest compilation of the best free and open source software in the universe. Each article is supplied with a legendary ratings chart helping you to make informed decisions.
ReviewsHundreds of in-depth reviews offering our unbiased and expert opinion on software. We offer helpful and impartial information.
Alternatives to Proprietary SoftwareReplace proprietary software with open source alternatives: Google, Microsoft, Apple, Adobe, IBM, Autodesk, Oracle, Atlassian, Corel, Cisco, Intuit, and SAS.
Linux Around The WorldLinux Around The World showcases events and usergroups that are Linux-related. This is a new series.
DockerGetting Started with Docker helps you master Docker, a set of platform as a service products that delivers software in packages called containers.
System ToolsEssential Linux system tools focuses on small, indispensable utilities, useful for system administrators as well as regular users.
ProductivityLinux utilities to maximise your productivity. Small, indispensable tools, useful for anyone running a Linux machine.
Home ComputersHome computers became commonplace in the 1980s. Emulate home computers including the Commodore 64, Amiga, Atari ST, ZX81, Amstrad CPC, and ZX Spectrum.
Now and ThenNow and Then examines how promising open source software fared over the years. It can be a bumpy ride.
Linux at HomeLinux at Home looks at a range of home activities where Linux can play its part, making the most of our time at home, keeping active and engaged.
Linux CandyLinux Candy reveals the lighter side of Linux. Have some fun and escape from the daily drudgery.
Android AppsBest Free Android Apps. We showcase free Android apps that are definitely worth downloading. There's a strict eligibility criteria for inclusion in this series.
Programming BooksThese best free books accelerate your learning of every programming language. Learn a new language today!
Programming TutorialsThese free tutorials offer the perfect tonic to our free programming books series.
Stars and StripesStars and Stripes is an occasional series looking at the impact of Linux in the USA.
Share this article