cuda_ml_rand_forest: Train a random forest model

In cuda.ml: R Interface for the RAPIDS cuML Suite of Libraries

Train a random forest model

Description

Trains a cuML random forest for classification or regression and returns an nvForest-backed model for inference.

Usage

cuda_ml_rand_forest(x, ...)

## Default S3 method:
cuda_ml_rand_forest(x, ...)

## S3 method for class 'data.frame'
cuda_ml_rand_forest(
  x,
  y,
  mtry = NULL,
  trees = 100L,
  min_n = 2L,
  bootstrap = TRUE,
  sample_fraction = 1,
  max_depth = 16L,
  max_leaves = Inf,
  n_bins = 128L,
  min_samples_leaf = 1L,
  split_criterion = NULL,
  min_impurity_decrease = 0,
  max_batch_size = 4096L,
  n_streams = 4L,
  seed = NULL,
  ...
)

## S3 method for class 'matrix'
cuda_ml_rand_forest(
  x,
  y,
  mtry = NULL,
  trees = 100L,
  min_n = 2L,
  bootstrap = TRUE,
  sample_fraction = 1,
  max_depth = 16L,
  max_leaves = Inf,
  n_bins = 128L,
  min_samples_leaf = 1L,
  split_criterion = NULL,
  min_impurity_decrease = 0,
  max_batch_size = 4096L,
  n_streams = 4L,
  seed = NULL,
  ...
)

## S3 method for class 'formula'
cuda_ml_rand_forest(
  formula,
  data,
  mtry = NULL,
  trees = 100L,
  min_n = 2L,
  bootstrap = TRUE,
  sample_fraction = 1,
  max_depth = 16L,
  max_leaves = Inf,
  n_bins = 128L,
  min_samples_leaf = 1L,
  split_criterion = NULL,
  min_impurity_decrease = 0,
  max_batch_size = 4096L,
  n_streams = 4L,
  seed = NULL,
  ...
)

## S3 method for class 'recipe'
cuda_ml_rand_forest(
  x,
  data,
  mtry = NULL,
  trees = 100L,
  min_n = 2L,
  bootstrap = TRUE,
  sample_fraction = 1,
  max_depth = 16L,
  max_leaves = Inf,
  n_bins = 128L,
  min_samples_leaf = 1L,
  split_criterion = NULL,
  min_impurity_decrease = 0,
  max_batch_size = 4096L,
  n_streams = 4L,
  seed = NULL,
  ...
)

Arguments

x	Depending on the context: A data frame of predictors. A matrix of predictors. A recipe specifying a set of preprocessing steps created from recipes::recipe(). A formula specifying the predictors and the outcome.
...	Optional arguments; currently unused.
y	A numeric vector (for regression) or factor (for classification) of desired responses.
mtry	Number of predictors sampled at each split. When NULL, classification uses the square root of the predictor count and regression uses all predictors.
trees	Number of trees. Default: 100L.
min_n	Minimum observations required to split a node. Default: 2L.
bootstrap	Whether to sample observations with replacement.
sample_fraction	Proportion of rows used for each tree, between 0 and 1. This is separate from mtry, which controls predictor sampling.
max_depth	Maximum tree depth. Default: 16L.
max_leaves	Maximum leaves per tree, or Inf for no limit.
n_bins	Number of candidate split bins. Default: 128L.
min_samples_leaf	Minimum observations in a leaf. Default: 1L.
split_criterion	Split criterion, or NULL for the mode default. Classification supports "gini" and "entropy"; regression supports "mse", "poisson", "gamma", and "inverse_gaussian".
min_impurity_decrease	Minimum impurity decrease required for a split.
max_batch_size	Maximum nodes processed in one batch. Default: 4096L.
n_streams	Number of CUDA streams used while fitting. Default: 4L.
seed	Random seed forwarded to cuML. When NULL, a seed is drawn from R's random-number generator, so set.seed() controls the fit.
formula	A formula specifying the outcome terms on the left-hand side, and the predictor terms on the right-hand side.
data	When a recipe or formula is used, data is specified as a data frame containing the predictors and (if applicable) the outcome.

Value

A random forest model for use with predict().

Deployment

Training uses cuML and requires the complete GPU backend installed by cuda_ml_install(). Persist the fitted model with cuda_ml_serialize(); the current state is device neutral. A host without a GPU can install the separate CPU inference backend with cuda_ml_install(device = "cpu") and restore the state with cuda_ml_unserialize(state, device = "cpu"). The CPU backend is roughly 3 MiB installed and does not include cuML or the complete managed CUDA and RAPIDS runtime. To create an independently usable Treelite checkpoint and a cuda.ml JSON sidecar instead, use cuda_ml_nvforest_export() and cuda_ml_nvforest_import().

cuda.ml documentation built on Aug. 21, 2026, 9:14 a.m.