simple-regression | Simple regression models in python | Machine Learning library

 by   MrChrisJohnson Python Version: Current License: No License

kandi X-RAY | simple-regression Summary

kandi X-RAY | simple-regression Summary

simple-regression is a Python library typically used in Artificial Intelligence, Machine Learning applications. simple-regression has no bugs, it has no vulnerabilities and it has low support. However simple-regression build file is not available. You can download it from GitHub.

Simple regression models in python.
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              simple-regression has a low active ecosystem.
              It has 5 star(s) with 1 fork(s). There are 2 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              simple-regression has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of simple-regression is current.

            kandi-Quality Quality

              simple-regression has no bugs reported.

            kandi-Security Security

              simple-regression has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              simple-regression does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              simple-regression releases are not available. You will need to build from source code and install.
              simple-regression has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed simple-regression and discovered the below as its top functions. This is intended to give you an instant insight into simple-regression implemented functionality, and help decide if they suit your requirements.
            • Predict item
            • Normalize a vector
            • Predict item from test vector
            Get all kandi verified functions for this library.

            simple-regression Key Features

            No Key Features are available at this moment for simple-regression.

            simple-regression Examples and Code Snippets

            No Code Snippets are available at this moment for simple-regression.

            Community Discussions

            QUESTION

            Use broom and tidyverse to run regressions on different dependent variables
            Asked 2018-Aug-01 at 21:58

            I'm looking for a Tidyverse / broom solution that can solve this puzzle:

            Let's say I have different DVs and a specific set of IVS and I want to perform a regression that considers every DV and this specific set of IVs. I know I can use something like for i in or apply family, but I really want to run that using tidyverse.

            The following code works as an example

            ...

            ANSWER

            Answered 2018-Aug-01 at 21:06

            We can loop through the column names that are dependent variables, use paste to create the formula to be passed into lm and get the summary statistics with tidy (from broom)

            Source https://stackoverflow.com/questions/51642146

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

            Vulnerabilities

            No vulnerabilities reported

            Install simple-regression

            You can download it from GitHub.
            You can use simple-regression 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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