Axing an randomForest.

randomForest objects are created from the randomForest package, which is used to train random forests based on Breiman's 2001 work. The package supports ensembles of classification and regression trees.

Usage

# S3 method for randomForest
axe_call(x, verbose = FALSE, ...)

# S3 method for randomForest
axe_ctrl(x, verbose = FALSE, ...)

# S3 method for randomForest
axe_env(x, verbose = FALSE, ...)

Arguments

x

A model object.

verbose

Print information each time an axe method is executed. Notes how much memory is released and what functions are disabled. Default is FALSE.

...

Any additional arguments related to axing.

Value

Axed randomForest object.

Examples

# Load libraries
library(parsnip)
library(rsample)
library(randomForest)
#> randomForest 4.7-1.1
#> Type rfNews() to see new features/changes/bug fixes.
#> 
#> Attaching package: ‘randomForest’
#> The following object is masked from ‘package:ggplot2’:
#> 
#>     margin
data(kyphosis, package = "rpart")

# Load data
set.seed(1234)
split <- initial_split(kyphosis, prop = 9/10)
spine_train <- training(split)

# Create model and fit
randomForest_fit <- rand_forest(mode = "classification",
                                mtry = 2,
                                trees = 2,
                                min_n = 3) %>%
  set_engine("randomForest") %>%
  fit_xy(x = spine_train[,2:4], y = spine_train$Kyphosis)

out <- butcher(randomForest_fit, verbose = TRUE)
#> ✔ Memory released: 192 B

# Another randomForest object
wrapped_rf <- function() {
  some_junk_in_environment <- runif(1e6)
  randomForest_fit <- randomForest(mpg ~ ., data = mtcars)
  return(randomForest_fit)
}

# Remove junk
cleaned_rf <- axe_env(wrapped_rf(), verbose = TRUE)
#> ✔ Memory released: 8.06 MB

# Check size
lobstr::obj_size(cleaned_rf)
#> 428 kB