By default, internal parallelization is switched off (`num.threads = 1`), `verbose` output is disabled, `respect.unordered.factors` is set to `order` for all splitrules.

Argument of: ranger::ranger

Number of trees.

Argument of: ranger::ranger

Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables.

Argument of: ranger::ranger

Variable importance mode, one of 'none', 'impurity', 'impurity_corrected', 'permutation'. The 'impurity' measure is the Gini index for classification, the variance of the responses for regression and the sum of test statistics (see splitrule) for survival.

Argument of: ranger::ranger

Save ranger.forest object, required for prediction. Set to FALSE to reduce memory usage if no prediction intended.

Argument of: ranger::ranger

Minimal node size. Default 1 for classification, 5 for regression, 3 for survival, and 10 for probability.

Argument of: ranger::ranger

Sample with replacement.

Argument of: ranger::ranger

Fraction of observations to sample. Default is 1 for sampling with replacement and 0.632 for sampling without replacement. For classification, this can be a vector of class-specific values.

Argument of: ranger::ranger

Splitting rule. For classification and probability estimation "gini" or "extratrees" with default "gini". For regression "variance", "extratrees" or "maxstat" with default "variance". For survival "logrank", "extratrees", "C" or "maxstat" with default "logrank".

Argument of: ranger::ranger

For "extratrees" splitrule.: Number of random splits to consider for each candidate splitting variable.

Argument of: ranger::ranger

For "maxstat" splitrule: Significance threshold to allow splitting.

Argument of: ranger::ranger

For "maxstat" splitrule: Lower quantile of covariate distribution to be considered for splitting.

Argument of: ranger::ranger

Numeric vector with weights between 0 and 1, representing the probability to select variables for splitting. Alternatively, a list of size num.trees, containing split select weight vectors for each tree can be used.