r descr_models("rand_forest", "randomForest")
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.
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.
rand_forest( mtry = integer(1), trees = integer(1), min_n = integer(1) ) %>% set_engine("randomForest") %>% set_mode("classification") %>% translate()
The "Fitting and Predicting with parsnip" article contains examples for rand_forest()
with the "randomForest"
engine.
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