scikit-feature | open-source feature selection repository in python

 by   jundongl Python Version: Current License: GPL-2.0

kandi X-RAY | scikit-feature Summary

kandi X-RAY | scikit-feature Summary

scikit-feature is a Python library typically used in Data Science applications. scikit-feature has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has high support. You can download it from GitHub.

open-source feature selection repository in python

            kandi-support Support

              scikit-feature has a highly active ecosystem.
              It has 1344 star(s) with 448 fork(s). There are 60 watchers for this library.
              It had no major release in the last 6 months.
              There are 34 open issues and 21 have been closed. On average issues are closed in 119 days. There are 9 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of scikit-feature is current.

            kandi-Quality Quality

              scikit-feature has 0 bugs and 0 code smells.

            kandi-Security Security

              scikit-feature has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              scikit-feature code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              scikit-feature is licensed under the GPL-2.0 License. This license is Strong Copyleft.
              Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

            kandi-Reuse Reuse

              scikit-feature releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed scikit-feature and discovered the below as its top functions. This is intended to give you an instant insight into scikit-feature implemented functionality, and help decide if they suit your requirements.
            • Constructs a weight matrix
            • Proximal gradient descent .
            • Performs the tree search algorithm
            • Performs a linear regression search on the features .
            • Compute the LCSI correlation coefficient .
            • r Compute the graph of a network .
            • Calculate the affinity matrix
            • r Compute the correlation matrix for the affinity matrix .
            • The reliefF function .
            • Calculate a CMIM response .
            Get all kandi verified functions for this library.

            scikit-feature Key Features

            No Key Features are available at this moment for scikit-feature.

            scikit-feature Examples and Code Snippets

            No Code Snippets are available at this moment for scikit-feature.

            Community Discussions


            Fisher score error - Length of value does not match length of index?
            Asked 2022-Mar-03 at 13:46

            I am working on ML problem, trying to compute the fisher score for feature selection purpose



            Answered 2022-Mar-03 at 13:46

            The inputs to the fisher_score method is expected a numpy array not a pandas dataframe/series.

            Try this:


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


            No vulnerabilities reported

            Install scikit-feature

            You can download it from GitHub.
            You can use scikit-feature 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.


            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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            gh repo clone jundongl/scikit-feature

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