SVM-python | In particular , the SMO algorithm | Machine Learning library
kandi X-RAY | SVM-python Summary
kandi X-RAY | SVM-python Summary
SVM-python is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Example Codes applications. SVM-python has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However SVM-python build file is not available. You can download it from GitHub.
This project implements the SMO algorithm for SVM in Python.
This project implements the SMO algorithm for SVM in Python.
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SVM-python has a low active ecosystem.
It has 35 star(s) with 25 fork(s). There are 4 watchers for this library.
It had no major release in the last 6 months.
There are 1 open issues and 0 have been closed. On average issues are closed in 306 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of SVM-python is current.
Quality
SVM-python has 0 bugs and 0 code smells.
Security
SVM-python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
SVM-python code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
SVM-python is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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SVM-python releases are not available. You will need to build from source code and install.
SVM-python has no build file. You will be need to create the build yourself to build the component from source.
SVM-python saves you 352 person hours of effort in developing the same functionality from scratch.
It has 842 lines of code, 28 functions and 5 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed SVM-python and discovered the below as its top functions. This is intended to give you an instant insight into SVM-python implemented functionality, and help decide if they suit your requirements.
- Calculate SMO2 .
- The SMO5 function
- Smo3 method
- Implements SMO4 .
- Implementation of SMO .
- Predict the decision function for the given test data .
Get all kandi verified functions for this library.
SVM-python Key Features
No Key Features are available at this moment for SVM-python.
SVM-python Examples and Code Snippets
No Code Snippets are available at this moment for SVM-python.
Community Discussions
Trending Discussions on SVM-python
QUESTION
How to draw the hyperplanes for SVM One-Versus-All?
Asked 2020-Dec-01 at 05:38
I was trying to draw the hyperplanes when SVM-OVA was performed as following:
...ANSWER
Answered 2020-Dec-01 at 05:38The problem is the C parameter of SVC is too small (by default 1.0). According to this post,
Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points.
Therefore, the solution is to use a much larger C, for example 1e5
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install SVM-python
You can download it from GitHub.
You can use SVM-python 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 SVM-python 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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