README.md

In edarf: Exploratory Data Analysis using Random Forests

Functions useful for exploratory data analysis using random forests.

This package extends the functionality of random forests fit by party (multivariate, regression, and classification), randomForestSRC (regression and classification,), randomForest (regression and classification), and ranger (classification and regression).

The subdirectory pkg contains the actual package. The package can be installed with devtools.

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!

edarf documentation built on May 2, 2019, 2:39 a.m.