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FeatureSelection

Feature Selection in R using glmnet-lasso, xgboost and ranger

Install / Use

/learn @mlampros/FeatureSelection
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

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Feature Selection in R using glmnet-lasso, xgboost and ranger

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This R package wraps glmnet-lasso, xgboost and ranger to perform feature selection. After downloading use ? to read info about each function (i.e. ?feature_selection). More details can be found in the blog-post (http://mlampros.github.io/2016/02/14/feature-selection/). To download the latest version from Github use,

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remotes::install_github('mlampros/FeatureSelection')

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Package Updates:

  • Currently there is a new version of glmnet (3.0.0) with new functionality (relax, trace, assess, bigGlm), however it requires an R version of 3.6.0 (see the new vignette for more information).
  • In the ranger R package the ranger::importance_pvalues() was added
  • Currently, the recommended approach for future selection is SHAP
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UPDATE 03-02-2020

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Docker images of the FeatureSelection package are available to download from my dockerhub account. The images come with Rstudio and the R-development version (latest) installed. The whole process was tested on Ubuntu 18.04. To pull & run the image do the following,

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docker pull mlampros/featureselection:rstudiodev

docker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 mlampros/featureselection:rstudiodev

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The user can also bind a home directory / folder to the image to use its files by specifying the -v command,

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docker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 -v /home/YOUR_DIR:/home/rstudio/YOUR_DIR mlampros/featureselection:rstudiodev


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In the latter case you might have first give permission privileges for write access to YOUR_DIR directory (not necessarily) using,

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chmod -R 777 /home/YOUR_DIR


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The USER defaults to rstudio but you have to give your PASSWORD of preference (see www.rocker-project.org for more information).

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Open your web-browser and depending where the docker image was build / run give,

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1st. Option on your personal computer,

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http://0.0.0.0:8787 

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2nd. Option on a cloud instance,

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http://Public DNS:8787

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to access the Rstudio console in order to give your username and password.

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View on GitHub
GitHub Stars56
CategoryDevelopment
Updated1y ago
Forks27

Languages

R

Security Score

70/100

Audited on Feb 25, 2025

No findings