py_mob | Python Implementation of Monotonic Optimal Binning

by statcompute Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | py_mob Summary

py_mob is a Python library. py_mob has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However py_mob build file is not available. You can install using 'pip install py_mob' or download it from GitHub, PyPI.

monotonic optimal binning (py_mob). as an attempt to mimic the mob r package (the py_mob is a collection of python functions that would generate the monotonic binning and perform the woe (weight of evidence) transformation used in consumer credit scorecard developments. the woe transformation is a piecewise transformation that is linear to the log odds. for a numeric variable, all of its monotonic functional transformations will converge to the same woe transformation. in addition, information value and ks statistic of each independent variables is also calculated to evaluate the variable predictiveness. different from other python packages for the same purpose, the py_mob package is very lightweight

Support

Quality

Security

License

Reuse

Support

py_mob has a low active ecosystem.

It has 10 star(s) with 6 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of py_mob is current.

Quality

py_mob has 0 bugs and 1 code smells.

Security

py_mob has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

py_mob code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

py_mob is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

py_mob releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

py_mob has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 221 lines of code, 16 functions and 2 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Calculate k - means clustering .
Implementation of GBMM regressor .
Binary binning .
Calculate the isotopic binomial distribution .
Binary binning .
Perform binning on the input data .
Calculate the woeffoe coefficient .
Performs automatic binning .
Generate rule .
Generate the woe coefficient .

Get all kandi verified functions for this library.

py_mob Key Features

No Key Features are available at this moment for py_mob.

py_mob Examples and Code Snippets

No Code Snippets are available at this moment for py_mob.

Community Discussions

No Community Discussions are available at this moment for py_mob.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install py_mob

You can install using 'pip install py_mob' or download it from GitHub, PyPI.
You can use py_mob 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 .

Find more information at: