logitboost | LogitBoost classification algorithm built on top | Machine Learning library
kandi X-RAY | logitboost Summary
kandi X-RAY | logitboost Summary
logitboost is a Python library typically used in Artificial Intelligence, Machine Learning applications. logitboost has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install logitboost' or download it from GitHub, PyPI.
LogitBoost classification algorithm built on top of scikit-learn
LogitBoost classification algorithm built on top of scikit-learn
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Quality
Security
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Support
logitboost has a low active ecosystem.
It has 9 star(s) with 7 fork(s). There are 2 watchers for this library.
It had no major release in the last 12 months.
There are 0 open issues and 1 have been closed. On average issues are closed in 526 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of logitboost is 0.7
Quality
logitboost has 0 bugs and 18 code smells.
Security
logitboost has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
logitboost code analysis shows 0 unresolved vulnerabilities.
There are 1 security hotspots that need review.
License
logitboost 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
logitboost 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.
It has 537 lines of code, 35 functions and 8 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed logitboost and discovered the below as its top functions. This is intended to give you an instant insight into logitboost implemented functionality, and help decide if they suit your requirements.
- Return information about the Python version
- Calculates the log - probability of the model
- Calculates the decision function
- Compute the probability of the decision function
- Predict classes for the decision function
- Return a function to resolve the linkcode
- Get git revision from git
- Helper function to get package variable
- Read a file
- Resolve a path
Get all kandi verified functions for this library.
logitboost Key Features
No Key Features are available at this moment for logitboost.
logitboost Examples and Code Snippets
No Code Snippets are available at this moment for logitboost.
Community Discussions
Trending Discussions on logitboost
QUESTION
How to extract the feature importances from the Logitboost algorithm in a multi-class classification setting?
Asked 2020-May-27 at 22:21
I am currently running a multi-class Logitboost algorithm (docs), which works great. However, when trying to view the importances of different features I get this error message:
...ANSWER
Answered 2020-May-27 at 22:21By looking a bit into the source code, the base_estimator defaults to a DecisionTree:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install logitboost
You can install using 'pip install logitboost' or download it from GitHub, PyPI.
You can use logitboost 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 logitboost 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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