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(), case.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(), case.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.

### Examples

The “Fitting and Predicting with parsnip” article contains examples for rand_forest() with the "ranger" engine.

### References

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.