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scikit-learn | scikit-learn : machine learning in Python | Machine Learning library

 by   scikit-learn Python Version: 1.2.0rc1 License: BSD-3-Clause

 by   scikit-learn Python Version: 1.2.0rc1 License: BSD-3-Clause

kandi X-RAY | scikit-learn Summary

scikit-learn is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Pandas applications. scikit-learn has no bugs, it has build file available, it has a Permissive License and it has high support. However scikit-learn has 1 vulnerabilities. You can install using 'pip install scikit-learn' or download it from GitHub, PyPI.
scikit-learn: machine learning in Python
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • scikit-learn has a highly active ecosystem.
  • It has 52681 star(s) with 23878 fork(s). There are 2164 watchers for this library.
  • There were 4 major release(s) in the last 6 months.
  • There are 1541 open issues and 8351 have been closed. On average issues are closed in 291 days. There are 600 open pull requests and 0 closed requests.
  • It has a positive sentiment in the developer community.
  • The latest version of scikit-learn is 1.2.0rc1
scikit-learn Support
Best in #Machine Learning
Average in #Machine Learning
scikit-learn Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • scikit-learn has 0 bugs and 0 code smells.
scikit-learn Quality
Best in #Machine Learning
Average in #Machine Learning
scikit-learn Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • scikit-learn has 1 vulnerability issues reported (1 critical, 0 high, 0 medium, 0 low).
  • scikit-learn code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
scikit-learn Security
Best in #Machine Learning
Average in #Machine Learning
scikit-learn Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • scikit-learn is licensed under the BSD-3-Clause License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
scikit-learn License
Best in #Machine Learning
Average in #Machine Learning
scikit-learn License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • scikit-learn releases are available to install and integrate.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • scikit-learn saves you 147220 person hours of effort in developing the same functionality from scratch.
  • It has 176758 lines of code, 9057 functions and 897 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
scikit-learn Reuse
Best in #Machine Learning
Average in #Machine Learning
scikit-learn Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

kandi has reviewed scikit-learn and discovered the below as its top functions. This is intended to give you an instant insight into scikit-learn implemented functionality, and help decide if they suit your requirements.

  • Linear Grammarization problem .
    • Linear path solver .
      • Logistic regression .
        • Compute a dictionary of learning statistics for a given dataset .
          • Local embedding .
            • Plot the image .
              • Plot the partial dependence of the estimator .
                • Check if an array is valid .
                  • r Enet Path Method .
                    • Calculate partial dependence of the estimator .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      scikit-learn Key Features

                      scikit-learn: machine learning in Python

                      scikit-learn Examples and Code Snippets

                      See all related Code Snippets

                      Community Discussions

                      Trending Discussions on scikit-learn
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                      • Sklearn: Calibrate a multi-label classification with CalibratedClassifierCV
                      • understanding sklearn calibratedClassifierCV
                      • Meaning of `penalty` and `loss` in LinearSVC
                      Trending Discussions on scikit-learn

                      QUESTION

                      Installing scipy and scikit-learn on apple m1

                      Asked 2022-Mar-22 at 06:21

                      The installation on the m1 chip for the following packages: Numpy 1.21.1, pandas 1.3.0, torch 1.9.0 and a few other ones works fine for me. They also seem to work properly while testing them. However when I try to install scipy or scikit-learn via pip this error appears:

                      ERROR: Failed building wheel for numpy

                      Failed to build numpy

                      ERROR: Could not build wheels for numpy which use PEP 517 and cannot be installed directly

                      Why should Numpy be build again when I have the latest version from pip already installed?

                      Every previous installation was done using python3.9 -m pip install ... on Mac OS 11.3.1 with the apple m1 chip.

                      Maybe somebody knows how to deal with this error or if its just a matter of time.

                      ANSWER

                      Answered 2021-Aug-02 at 14:33

                      Please see this note of scikit-learn about

                      Installing on Apple Silicon M1 hardware

                      The recently introduced macos/arm64 platform (sometimes also known as macos/aarch64) requires the open source community to upgrade the build configuation and automation to properly support it.

                      At the time of writing (January 2021), the only way to get a working installation of scikit-learn on this hardware is to install scikit-learn and its dependencies from the conda-forge distribution, for instance using the miniforge installers:

                      https://github.com/conda-forge/miniforge

                      The following issue tracks progress on making it possible to install scikit-learn from PyPI with pip:

                      https://github.com/scikit-learn/scikit-learn/issues/19137

                      Source https://stackoverflow.com/questions/68620927

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      No vulnerabilities reported

                      Install scikit-learn

                      You can install using 'pip install scikit-learn' or download it from GitHub, PyPI.
                      You can use scikit-learn like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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                      Install
                      • pip install scikit-learn

                      Clone
                      • https://github.com/scikit-learn/scikit-learn.git

                      • gh repo clone scikit-learn/scikit-learn

                      • git@github.com:scikit-learn/scikit-learn.git

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