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
)
MLModel
class object.
number of trees.
number of variables to possibly split at in each node.
variable importance mode.
minimum node size.
logical indicating whether to sample with replacement.
fraction of observations to sample.
splitting rule.
number of random splits to consider for each
candidate splitting variable in the "extratrees"
rule.
significance threshold to allow splitting in the
"maxstat"
rule.
lower quantile of covariate distribution to be considered for
splitting in the "maxstat"
rule.
numeric vector with weights between 0 and 1, representing the probability to select variables for splitting.
character vector with variable names to be
always selected in addition to the mtry
variables tried for
splitting.
handling of unordered factor covariates.
scale permutation importance by standard error.
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.
ranger
, fit
,
resample
# \donttest{
## Requires prior installation of suggested package ranger to run
fit(Species ~ ., data = iris, model = RangerModel)
# }
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