https://github.com/zmjones/edarf

exploratory data analysis using random forests
https://github.com/zmjones/edarf

exploratory-data-analysis machine-learning r random-forest rstats

Last synced: 3 months ago
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exploratory data analysis using random forests

• Host: GitHub
• URL: https://github.com/zmjones/edarf
• Owner: zmjones
• License: mit
• Created: 2014-09-04T16:03:18.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2018-02-06T20:54:56.000Z (over 6 years ago)
• Last Synced: 2024-01-20T14:25:52.427Z (6 months ago)
• Topics: exploratory-data-analysis, machine-learning, r, random-forest, rstats
• Language: R
• Size: 6.92 MB
• Stars: 67
• Watchers: 13
• Forks: 11
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-stars - zmjones/edarf - exploratory data analysis using random forests (R)

README

        
![](https://travis-ci.org/zmjones/edarf.svg) ![](http://www.r-pkg.org/badges/version/edarf)

![](http://cranlogs.r-pkg.org/badges/edarf)

[![DOI](https://zenodo.org/badge/23669422.svg)](https://zenodo.org/badge/latestdoi/23669422)

[![status](http://joss.theoj.org/papers/d29df349c8450ef958c0fde5bf164371/status.svg)](http://joss.theoj.org/papers/d29df349c8450ef958c0fde5bf164371)

Functions useful for exploratory data analysis using random forests.

This package extends the functionality of random forests fit by [party](https://CRAN.R-project.org/package=party) (multivariate, regression, and classification), [randomForestSRC](https://CRAN.R-project.org/package=randomForestSRC) (regression and classification,), [randomForest](https://CRAN.R-project.org/package=randomForest) (regression and classification), and [ranger](https://cran.r-project.org/package=ranger) (classification and regression).

The subdirectory `pkg` contains the actual package. The package can be installed with [devtools](https://cran.r-project.org/package=devtools).

```{r}

devtools::install_github("zmjones/edarf", subdir = "pkg")

```

Functionality includes:

 - `partial_dependence` which computes the expected prediction made by the random forest if it were marginalized to only depend on a subset of the features. `plot_pd` plots the results.

 - `variable_importance` which computes feature importance for arbitrary loss functions, aggregated across the training data or for individual observations. This may also be used for subsets of the feature space in order to detect interactions.

 - `extract_proximity` and `plot_prox` which computes or extracts proximity matrices and plots them using a biplot given a matrix of principal components of said matrix.

 

If you use the package for research, please cite it.

	@article{jones2016,

	  doi = {10.21105/joss.00092},

	  url = {http://dx.doi.org/10.21105/joss.00092},

	  year  = {2016},

	  month = {oct},

	  publisher = {The Open Journal},

	  volume = {1},

	  number = {6},

	  author = {Zachary M. Jones and Fridolin J. Linder},

	  title = {edarf: Exploratory Data Analysis using Random Forests},

	  journal = {The Journal of Open Source Software}

	}

Pull requests, bug reports, feature requests, etc. are welcome!