# Load libraries
suppressWarnings(suppressMessages(library(parsnip)))
suppressWarnings(suppressMessages(library(rsample)))
# Load data
set.seed(1234)
split <- initial_split(iris, prop = 9/10)
iris_train <- training(split)
# Create model and fit
ranger_fit <- rand_forest(mode = "classification",
mtry = 2,
trees = 20,
min_n = 3) %>%
set_engine("ranger") %>%
fit(Species ~ ., data = iris_train)
# Save
save(ranger_fit, file = "inst/extdata/ranger.rda")
# Another example
ranger_reg_fit <- rand_forest(mode = "regression") %>%
update(min_n = max(8, floor(.obs()/10))) %>%
update(mtry = 4) %>%
set_engine("ranger") %>%
fit(Sepal.Width ~ ., data = iris)
# Save
save(ranger_reg_fit, file = "inst/extdata/ranger_reg.rda")
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