Rforestry: Random Forests, Linear Trees, and Gradient Boosting for Inference and Interpretability

Sören Künzel, Theo Saarinen, Simon Walter, Sam Antonyan, Edward Liu, Allen Tang, Jasjeet Sekhon

Introduction

Rforestry is a fast implementation of Honest Random Forests, Gradient Boosting, and Linear Random Forests, with an emphasis on inference and interpretability.

How to install

  1. The GFortran compiler has to be up to date. GFortran Binaries can be found here.
  2. The devtools package has to be installed. You can install it using, install.packages("devtools").
  3. The package contains compiled code, and you must have a development environment to install the development version. You can use devtools::has_devel() to check whether you do. If no development environment exists, Windows users download and install Rtools and macOS users download and install Xcode.
  4. The latest development version can then be installed using devtools::install_github("forestry-labs/Rforestry"). For Windows users, you’ll need to skip 64-bit compilation devtools::install_github("forestry-labs/Rforestry", INSTALL_opts = c('--no-multiarch')) due to an outstanding gcc issue.

Python Package Usage 

library(Rforestry)




set.seed(292315)
test_idx <- sample(nrow(iris), 3)
x_train <- iris[-test_idx, -1]
y_train <- iris[-test_idx, 1]
x_test <- iris[test_idx, -1]

rf <- forestry(x = x_train, y = y_train, nthread = 2)

predict(rf, x_test)

R Package Usage 

library(Rforestry)




set.seed(292315)
test_idx <- sample(nrow(iris), 3)
x_train <- iris[-test_idx, -1]
y_train <- iris[-test_idx, 1]
x_test <- iris[test_idx, -1]

rf <- forestry(x = x_train, y = y_train, nthread = 2)

predict(rf, x_test)