RangerModel: Fast Random Forest Model

In MachineShop: Machine Learning Models and Tools

Fast Random Forest Model

Description

Fast implementation of random forests or recursive partitioning.

Usage

RangerModel(
  num.trees = 500,
  mtry = integer(),
  importance = c("impurity", "impurity_corrected", "permutation"),
  min.node.size = integer(),
  replace = TRUE,
  sample.fraction = if (replace) 1 else 0.632,
  splitrule = character(),
  num.random.splits = 1,
  alpha = 0.5,
  minprop = 0.1,
  split.select.weights = numeric(),
  always.split.variables = character(),
  respect.unordered.factors = character(),
  scale.permutation.importance = FALSE,
  verbose = FALSE
)

Arguments

num.trees	number of trees.
mtry	number of variables to possibly split at in each node.
importance	variable importance mode.
min.node.size	minimum node size.
replace	logical indicating whether to sample with replacement.
sample.fraction	fraction of observations to sample.
splitrule	splitting rule.
num.random.splits	number of random splits to consider for each candidate splitting variable in the "extratrees" rule.
alpha	significance threshold to allow splitting in the "maxstat" rule.
minprop	lower quantile of covariate distribution to be considered for splitting in the "maxstat" rule.
split.select.weights	numeric vector with weights between 0 and 1, representing the probability to select variables for splitting.
always.split.variables	character vector with variable names to be always selected in addition to the mtry variables tried for splitting.
respect.unordered.factors	handling of unordered factor covariates.
scale.permutation.importance	scale permutation importance by standard error.
verbose	show computation status and estimated runtime.

Details

Response types:

factor, numeric, Surv

Automatic tuning of grid parameters:

mtry, min.node.size*, splitrule*

* excluded from grids by default

Default argument values and further model details can be found in the source See Also link below.

Value

MLModel class object.

See Also

ranger, fit, resample

Examples

## Requires prior installation of suggested package ranger to run

fit(Species ~ ., data = iris, model = RangerModel)

MachineShop documentation built on Sept. 11, 2024, 6:28 p.m.