Welcome to scikit-learn

• Installing scikit-learn
  • Installing the latest release
  • Installing on Apple Silicon M1 hardware
  • Third party distributions of scikit-learn
  • Troubleshooting
• Frequently Asked Questions
  • What is the project name (a lot of people get it wrong)?
  • How do you pronounce the project name?
  • Why scikit?
  • How can I contribute to scikit-learn?
  • What’s the best way to get help on scikit-learn usage?
  • How should I save, export or deploy estimators for production?
  • How can I create a bunch object?
  • How can I load my own datasets into a format usable by scikit-learn?
  • What are the inclusion criteria for new algorithms ?
  • Why are you so selective on what algorithms you include in scikit-learn?
  • Why did you remove HMMs from scikit-learn?
  • Will you add graphical models or sequence prediction to scikit-learn?
  • Will you add GPU support?
  • Do you support PyPy?
  • How do I deal with string data (or trees, graphs…)?
  • Why do I sometime get a crash/freeze with n_jobs > 1 under OSX or Linux?
  • Why does my job use more cores than specified with n_jobs?
  • Why is there no support for deep or reinforcement learning / Will there be support for deep or reinforcement learning in scikit-learn?
  • Why is my pull request not getting any attention?
  • How do I set a random_state for an entire execution?
  • Why do categorical variables need preprocessing in scikit-learn, compared to other tools?
  • Why does Scikit-learn not directly work with, for example, pandas.DataFrame?
  • Do you plan to implement transform for target y in a pipeline?
  • Why are there so many different estimators for linear models?
• Support
  • Mailing List
  • User questions
  • Bug tracker
  • Gitter
  • Documentation resources
• Related Projects
  • Interoperability and framework enhancements
  • Other estimators and tasks
  • Statistical learning with Python
  • Translations of scikit-learn documentation
• About us
  • History
  • Governance
  • Authors
  • Contributor Experience Team
  • Communication Team
  • Emeritus Core Developers
  • Emeritus Communication Team
  • Citing scikit-learn
  • Artwork
  • Funding
  • Sprints
  • Infrastructure support
• Who is using scikit-learn?
  • J.P.Morgan
  • Spotify
  • Inria
  • betaworks
  • Hugging Face
  • Evernote
  • Télécom ParisTech
  • Booking.com
  • AWeber
  • Yhat
  • Rangespan
  • Birchbox
  • Bestofmedia Group
  • Change.org
  • PHIMECA Engineering
  • HowAboutWe
  • PeerIndex
  • DataRobot
  • OkCupid
  • Lovely
  • Data Publica
  • Machinalis
  • solido
  • INFONEA
  • Dataiku
  • Otto Group
  • Zopa
  • MARS
  • BNP Paribas Cardif
• Release History
  • Version 1.1.3
  • Version 1.1.2
  • Version 1.1.1
  • Version 1.1.0
  • Version 1.0.2
  • Version 1.0.1
  • Version 1.0.0
  • Version 0.24.2
  • Version 0.24.1
  • Version 0.24.0
  • Version 0.23.2
  • Version 0.23.1
  • Version 0.23.0
  • Version 0.22.2.post1
  • Version 0.22.1
  • Version 0.22.0
  • Version 0.21.3
  • Version 0.21.2
  • Version 0.21.1
  • Version 0.21.0
  • Version 0.20.4
  • Version 0.20.3
  • Version 0.20.2
  • Version 0.20.1
  • Version 0.20.0
  • Version 0.19.2
  • Version 0.19.1
  • Version 0.19
  • Version 0.18.2
  • Version 0.18.1
  • Version 0.18
  • Version 0.17.1
  • Version 0.17
  • Version 0.16.1
  • Version 0.16
  • Version 0.15.2
  • Version 0.15.1
  • Version 0.15
  • Version 0.14
  • Version 0.13.1
  • Version 0.13
  • Version 0.12.1
  • Version 0.12
  • Version 0.11
  • Version 0.10
  • Version 0.9
  • Version 0.8
  • Version 0.7
  • Version 0.6
  • Version 0.5
  • Version 0.4
  • Earlier versions
• Roadmap
  • Purpose of this document
  • Statement of purpose: Scikit-learn in 2018
  • Architectural / general goals
  • Subpackage-specific goals
• Scikit-learn governance and decision-making
  • Roles And Responsibilities
  • Decision Making Process
  • Enhancement proposals (SLEPs)