entropy_based_binning | Entropy based binning of discrete variables
kandi X-RAY | entropy_based_binning Summary
kandi X-RAY | entropy_based_binning Summary
entropy_based_binning is a Python library. entropy_based_binning has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can install using 'pip install entropy_based_binning' or download it from GitHub, PyPI.
This module implements the functionality to exhaustively search for the highest entropy binning of a sequence of integers, such that.
This module implements the functionality to exhaustively search for the highest entropy binning of a sequence of integers, such that.
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Support
entropy_based_binning has a low active ecosystem.
It has 18 star(s) with 10 fork(s). There are 3 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 1 have been closed. On average issues are closed in 110 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of entropy_based_binning is current.
Quality
entropy_based_binning has 0 bugs and 0 code smells.
Security
entropy_based_binning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
entropy_based_binning code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
entropy_based_binning is licensed under the GPL-3.0 License. This license is Strong Copyleft.
Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.
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entropy_based_binning releases are not available. You will need to build from source code and install.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions are not available. Examples and code snippets are available.
entropy_based_binning saves you 37 person hours of effort in developing the same functionality from scratch.
It has 100 lines of code, 9 functions and 3 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed entropy_based_binning and discovered the below as its top functions. This is intended to give you an instant insight into entropy_based_binning implemented functionality, and help decide if they suit your requirements.
- Bins an array of arrays
- Perform binning on an array
- Generate bins for a sequence
- Apply binning to binning
- Calculate the h
- Evaluate binning
- Checks if x is an integer
Get all kandi verified functions for this library.
entropy_based_binning Key Features
No Key Features are available at this moment for entropy_based_binning.
entropy_based_binning Examples and Code Snippets
No Code Snippets are available at this moment for entropy_based_binning.
Community Discussions
Trending Discussions on entropy_based_binning
QUESTION
How to count the frequency of a class in a pandas dataset
Asked 2021-Oct-17 at 19:48
I am writing a program to discretize a set of attributes via entropy discretization. The goal is to parse the dataset
...ANSWER
Answered 2021-Oct-17 at 19:48Use value_counts:
QUESTION
How to get the first value from pandas value_counts()
Asked 2021-Oct-17 at 14:13
I am writing a program to discretize a set of attributes via entropy discretization. The goal is to parse the dataset
...ANSWER
Answered 2021-Oct-17 at 14:13Maybe try:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install entropy_based_binning
You can install using 'pip install entropy_based_binning' or download it from GitHub, PyPI.
You can use entropy_based_binning 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.
You can use entropy_based_binning 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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