In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("rand_forest", "randomForest")

Tuning Parameters

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

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

This model has r nrow(param) tuning parameters:

param$item

mtry depends on the number of columns and the model mode. The default in [randomForest::randomForest()] is floor(sqrt(ncol(x))) for classification and floor(ncol(x)/3) for regression.

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("randomForest") %>% 
  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("randomForest") %>% 
  set_mode("classification") %>% 
  translate()

Preprocessing requirements

Saving fitted model objects

Examples

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

References

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

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