ranger::ranger() fits a model that creates a large number of decision
trees, each independent of the others. The final prediction uses all
predictions from the individual trees and combines them.
Details
For this engine, there are multiple modes: classification and regression
Tuning Parameters
This model has 3 tuning parameters:
mtry: # Randomly Selected Predictors (type: integer, default: see below)trees: # Trees (type: integer, default: 500L)min_n: Minimal Node Size (type: integer, default: see below)
mtry depends on the number of columns. The default in
ranger::ranger() is floor(sqrt(ncol(x))).
min_n depends on the mode. For regression, a value of 5 is the
default. For classification, a value of 10 is used.
Translation from parsnip to the original package (regression)
rand_forest(
mtry = integer(1),
trees = integer(1),
min_n = integer(1)
) %>%
set_engine("ranger") %>%
set_mode("regression") %>%
translate()## Random Forest Model Specification (regression)
##
## Main Arguments:
## mtry = integer(1)
## trees = integer(1)
## min_n = integer(1)
##
## Computational engine: ranger
##
## Model fit template:
## ranger::ranger(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## mtry = min_cols(~integer(1), x), num.trees = integer(1),
## min.node.size = min_rows(~integer(1), x), num.threads = 1,
## verbose = FALSE, seed = sample.int(10^5, 1))min_rows() and min_cols() will adjust the number of neighbors if the
chosen value if it is not consistent with the actual data dimensions.
Translation from parsnip to the original package (classification)
rand_forest(
mtry = integer(1),
trees = integer(1),
min_n = integer(1)
) %>%
set_engine("ranger") %>%
set_mode("classification") %>%
translate()## Random Forest Model Specification (classification)
##
## Main Arguments:
## mtry = integer(1)
## trees = integer(1)
## min_n = integer(1)
##
## Computational engine: ranger
##
## Model fit template:
## ranger::ranger(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## mtry = min_cols(~integer(1), x), num.trees = integer(1),
## min.node.size = min_rows(~integer(1), x), num.threads = 1,
## verbose = FALSE, seed = sample.int(10^5, 1), probability = TRUE)Note that a ranger probability forest is always fit (unless the
probability argument is changed by the user via
set_engine()).
Preprocessing requirements
This engine does not require any special encoding of the predictors.
Categorical predictors can be partitioned into groups of factor levels
(e.g. {a, c} vs {b, d}) when splitting at a node. Dummy variables
are not required for this model.
Other notes
By default, parallel processing is turned off. When tuning, it is more
efficient to parallelize over the resamples and tuning parameters. To
parallelize the construction of the trees within the ranger model,
change the num.threads argument via set_engine().
For ranger confidence intervals, the intervals are constructed using
the form estimate +/- z * std_error. For classification probabilities,
these values can fall outside of [0, 1] and will be coerced to be in
this range.
Case weights
This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on tidymodels.org.
The fit() and fit_xy() arguments have arguments called
case_weights that expect vectors of case weights.
Sparse Data
This model can utilize sparse data during model fitting and prediction.
Both sparse matrices such as dgCMatrix from the Matrix package and
sparse tibbles from the sparsevctrs package are supported. See
sparse_data for more information.
Saving fitted model objects
This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.
Examples
The “Fitting and Predicting with parsnip” article contains
examples
for rand_forest() with the "ranger" engine.