smlr | Python package for Sparse Multinomial Logistic Regression | Machine Learning library

by KamitaniLab Python Version: Current License: MIT

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kandi X-RAY | smlr Summary

smlr is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. smlr has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Sparse Multinomial Logistic Regression (SMLR) classifier, developed by Kei Majima at Kyoto Univ. Sparse regularization by automatic relevance determination (ARD) prior was introduced to the linear multinomial logistic regression algorithm (Yamashita et al., 2008). This regularization process estimates the importance of each voxel (feature) and prunes away voxels that are not useful for prediction. Original SLR toolbox for Matlab is available at

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Support

smlr has a low active ecosystem.

It has 12 star(s) with 4 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 3 have been closed. On average issues are closed in 5 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of smlr is current.

Quality

smlr has 0 bugs and 0 code smells.

Security

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

smlr code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

smlr 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

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

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Fit the feature model
Algorithm for theta step
Computes Hessian Hessian Hessian
Gradient of gradients
Evaluate the log likelihood function
Performs alpha step
Predict the class of a feature
Predict log probabilities for a feature
Compute the predictive probability of a feature

Get all kandi verified functions for this library.

smlr Key Features

No Key Features are available at this moment for smlr.

smlr Examples and Code Snippets

No Code Snippets are available at this moment for smlr.

Community Discussions

Trending Discussions on smlr

Different x and y axis scales in multifaceted scatter ggplot2

QUESTION

Different x and y axis scales in multifaceted scatter ggplot2

Asked 2020-Apr-21 at 12:15

I have used lemon package with ggplot2 for plotting multifaceted scatter plot with regression and confidence interval line using the following code

...

ANSWER

Answered 2020-Apr-21 at 12:15

I have solved this issue after taking help from this post.

First, create two plots using facet_grid and facet_wrap.

Source https://stackoverflow.com/questions/44257563

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

Vulnerabilities

No vulnerabilities reported

Install smlr

Run the following command:.

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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