Axing a C5.0.Source:
C5.0 objects are created from the
C50 package, which provides an
interface to the C5.0 classification model. The models that can be
generated include basic tree-based models as well as rule-based models.
# S3 method for C5.0 axe_call(x, verbose = FALSE, ...) # S3 method for C5.0 axe_ctrl(x, verbose = FALSE, ...) # S3 method for C5.0 axe_fitted(x, verbose = FALSE, ...)
A model object.
Print information each time an axe method is executed. Notes how much memory is released and what functions are disabled. Default is
Any additional arguments related to axing.
# Load libraries library(parsnip) library(rsample) library(rpart) # Load data set.seed(1234) split <- initial_split(kyphosis, props = 9/10) spine_train <- training(split) # Create model and fit c5_fit <- decision_tree(mode = "classification") %>% set_engine("C5.0") %>% fit(Kyphosis ~ ., data = spine_train) out <- butcher(c5_fit, verbose = TRUE) #> ✖ No memory released. Do not butcher. # Try another model from parsnip c5_fit2 <- boost_tree(mode = "classification", trees = 100) %>% set_engine("C5.0") %>% fit(Kyphosis ~ ., data = spine_train) out <- butcher(c5_fit2, verbose = TRUE) #> ✖ No memory released. Do not butcher. # Create model object from original library library(C50) library(modeldata) data(mlc_churn) c5_fit3 <- C5.0(x = mlc_churn[, -20], y = mlc_churn$churn) out <- butcher(c5_fit3, verbose = TRUE) #> ✔ Memory released: "6.28 kB" #> ✖ Disabled: `print()`, `summary()`, `C5.0Control()`, and `C5imp()`