View source: R/ML_RangerModel.R
RangerModel  R Documentation 
Fast implementation of random forests or recursive partitioning.
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
)
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 
alpha 
significance threshold to allow splitting in the

minprop 
lower quantile of covariate distribution to be considered for
splitting in the 
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 
respect.unordered.factors 
handling of unordered factor covariates. 
scale.permutation.importance 
scale permutation importance by standard error. 
verbose 
show computation status and estimated runtime. 
factor
, numeric
, Surv
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.
MLModel
class object.
ranger
, fit
,
resample
## Requires prior installation of suggested package ranger to run
fit(Species ~ ., data = iris, model = RangerModel)
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