View source: R/discretize_cart.R
step_discretize_cart  R Documentation 
step_discretize_cart()
creates a specification of a recipe step that will
discretize numeric data (e.g. integers or doubles) into bins in a supervised
way using a CART model.
step_discretize_cart(
recipe,
...,
role = NA,
trained = FALSE,
outcome = NULL,
cost_complexity = 0.01,
tree_depth = 10,
min_n = 20,
rules = NULL,
skip = FALSE,
id = rand_id("discretize_cart")
)
recipe 
A recipe object. The step will be added to the sequence of operations for this recipe. 
... 
One or more selector functions to choose which variables are
affected by the step. See 
role 
Defaults to 
trained 
A logical to indicate if the quantities for preprocessing have been estimated. 
outcome 
A call to 
cost_complexity 
The regularization parameter. Any split that does not
decrease the overall lack of fit by a factor of 
tree_depth 
The maximum depth in the final tree. Corresponds to

min_n 
The number of data points in a node required to continue
splitting. Corresponds to 
rules 
The splitting rules of the best CART tree to retain for each variable. If length zero, splitting could not be used on that column. 
skip 
A logical. Should the step be skipped when the recipe is baked by

id 
A character string that is unique to this step to identify it. 
step_discretize_cart()
creates nonuniform bins from numerical variables by
utilizing the information about the outcome variable and applying a CART
model.
The best selection of buckets for each variable is selected using the standard costcomplexity pruning of CART, which makes this discretization method resistant to overfitting.
This step requires the rpart package. If not installed, the step will stop with a note about installing the package.
Note that the original data will be replaced with the new bins.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble with columns terms
(the columns that is selected), values
is returned.
This step has 3 tuning parameters:
cost_complexity
: CostComplexity Parameter (type: double, default: 0.01)
tree_depth
: Tree Depth (type: integer, default: 10)
min_n
: Minimal Node Size (type: integer, default: 20)
This step performs an supervised operation that can utilize case weights.
To use them, see the documentation in recipes::case_weights and the examples on
tidymodels.org
.
step_discretize_xgb()
, recipes::recipe()
,
recipes::prep()
, recipes::bake()
library(modeldata)
data(ad_data)
library(rsample)
split < initial_split(ad_data, strata = "Class")
ad_data_tr < training(split)
ad_data_te < testing(split)
cart_rec <
recipe(Class ~ ., data = ad_data_tr) %>%
step_discretize_cart(
tau, age, p_tau, Ab_42,
outcome = "Class", id = "cart splits"
)
cart_rec < prep(cart_rec, training = ad_data_tr)
# The splits:
tidy(cart_rec, id = "cart splits")
bake(cart_rec, ad_data_te, tau)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.