README.md

In mlampros/FeatureSelection: Feature extraction and selection based on 'glmnet', 'xgboost' and 'ranger'

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

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,

remotes::install_github('mlampros/FeatureSelection')

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

UPDATE 03-02-2020

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,

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

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

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

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

chmod -R 777 /home/YOUR_DIR

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

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

1st. Option on your personal computer,

http://0.0.0.0:8787 

2nd. Option on a cloud instance,

http://Public DNS:8787

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

mlampros/FeatureSelection documentation built on Jan. 12, 2023, 4:40 a.m.