Skip to content
No description, website, or topics provided.
C++ R
Branch: master
Clone or download
Latest commit 0dc43e0 Jul 20, 2019

Travis-CI Build Status


forestry: Provides Functions for Fast Random Forests

Sören Künzel, Edward Liu, Theo Saarinen, Allen Tang, Jasjeet Sekhon


forestry is a fast implementation of Honest Random Forests.

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("soerenkuenzel/forestry") .


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)
weights = predict(rf, x_test, aggregation = "weightMatrix")$weightMatrix

weights %*% y_train
predict(rf, x_test)

Ridge Random Forest

A fast implementation of random forests using ridge penalized splitting and ridge regression for predictions.



n <- c(100)
a <- rnorm(n)
b <- rnorm(n)
c <- rnorm(n)
y <- 4*a + 5.5*b - .78*c
x <- data.frame(a,b,c)
forest <- forestry(x, y, ridgeRF = TRUE)
predict(forest, x)
You can’t perform that action at this time.