Train a random forest model — cuda_ml_rand

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().