In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("rand_forest", "ranger")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("mtry", "trees", "min_n"),
                 default = c("see below", "500L", "see below"))

param <-
  rand_forest() %>% 
  set_engine("ranger") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

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()

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()

Note that a ranger probability forest is always fit (unless the probability argument is changed by the user via [set_engine()]).

Preprocessing requirements

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

Saving fitted model objects

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.

parsnip documentation built on Aug. 18, 2023, 1:07 a.m.