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https://github.com/mlampros/featureselection
Feature Selection in R using glmnet-lasso, xgboost and ranger
https://github.com/mlampros/featureselection
feature r selection
Last synced: about 9 hours ago
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Feature Selection in R using glmnet-lasso, xgboost and ranger
- Host: GitHub
- URL: https://github.com/mlampros/featureselection
- Owner: mlampros
- Created: 2016-05-18T16:32:11.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-08-09T14:45:19.000Z (6 months ago)
- Last Synced: 2025-01-28T23:00:07.796Z (2 days ago)
- Topics: feature, r, selection
- Language: R
- Homepage: http://mlampros.github.io/FeatureSelection/
- Size: 150 KB
- Stars: 56
- Watchers: 4
- Forks: 27
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
Awesome Lists containing this project
README
[](https://github.com/mlampros/FeatureSelection/actions)
[](https://codecov.io/github/mlampros/FeatureSelection?branch=master)
[](https://hub.docker.com/r/mlampros/featureselection)
#### 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,
```R
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](https://cran.r-project.org/web/packages/glmnet/vignettes/relax.pdf) for more information).
* In the *ranger* R package the *ranger::importance_pvalues()* was added
* Currently, the recommended approach for future selection is [SHAP](https://github.com/slundberg/shap)
**UPDATE 03-02-2020**
**Docker images** of the *FeatureSelection* package are available to download from my [dockerhub](https://hub.docker.com/r/mlampros/featureselection) 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,
```R
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,
```R
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,
```R
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](https://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,
```R
http://0.0.0.0:8787
```
**2nd. Option** on a cloud instance,
```R
http://Public DNS:8787
```
to access the Rstudio console in order to give your username and password.