RangerModel {MachineShop}

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)