Discretize numeric variables with XgBoostSource:
step_discretize_xgb() creates a specification of a recipe step that will
discretize numeric data (e.g. integers or doubles) into bins in a supervised
way using an XgBoost model.
step_discretize_xgb( recipe, ..., role = NA, trained = FALSE, outcome = NULL, sample_val = 0.2, learn_rate = 0.3, num_breaks = 10, tree_depth = 1, min_n = 5, rules = NULL, skip = FALSE, id = rand_id("discretize_xgb") )
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
selections()for more details.
A logical to indicate if the quantities for preprocessing have been estimated.
A call to
varsto specify which variable is used as the outcome to train XgBoost models in order to discretize explanatory variables.
Share of data used for validation (with early stopping) of the learned splits (the rest is used for training). Defaults to 0.20.
The rate at which the boosting algorithm adapts from iteration-to-iteration. Corresponds to
etain the xgboost package. Defaults to 0.3.
The maximum number of discrete bins to bucket continuous features. Corresponds to
max_binin the xgboost package. Defaults to 10.
The maximum depth of the tree (i.e. number of splits). Corresponds to
max_depthin the xgboost package. Defaults to 1.
The minimum number of instances needed to be in each node. Corresponds to
min_child_weightin the xgboost package. Defaults to 5.
The splitting rules of the best XgBoost tree to retain for each variable.
A logical. Should the step be skipped when the recipe is baked by
recipes::bake()? While all operations are baked when
recipes::prep()is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using
skip = TRUEas it may affect the computations for subsequent operations
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of any existing operations.
step_discretize_xgb() creates non-uniform bins from numerical variables by
utilizing the information about the outcome variable and applying the xgboost
model. It is advised to impute missing values before this step. This step is
intended to be used particularly with linear models because thanks to
creating non-uniform bins it becomes easier to learn non-linear patterns from
The best selection of buckets for each variable is selected using an internal early stopping scheme implemented in the xgboost package, which makes this discretization method prone to overfitting.
The pre-defined values of the underlying xgboost learns good and reasonably
complex results. However, if one wishes to tune them the recommended path
would be to first start with changing the value of
num_breaks to e.g.: 20
or 30. If that doesn't give satisfactory results one could experiment with
min_n parameters. Note that it is not
recommended to tune
learn_rate simultaneously with other parameters.
This step requires the xgboost 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.
tidy() this step, a tibble with columns
(the columns that is selected),
values is returned.
This step has 5 tuning parameters:
sample_val: Proportion of data for validation (type: double, default: 0.2)
learn_rate: Learning Rate (type: double, default: 0.3)
num_breaks: Number of Cut Points (type: integer, default: 10)
tree_depth: Tree Depth (type: integer, default: 1)
min_n: Minimal Node Size (type: integer, default: 5)
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
library(rsample) library(recipes) data(credit_data, package = "modeldata") set.seed(1234) split <- initial_split(credit_data[1:1000, ], strata = "Status") credit_data_tr <- training(split) credit_data_te <- testing(split) xgb_rec <- recipe(Status ~ Income + Assets, data = credit_data_tr) %>% step_impute_median(Income, Assets) %>% step_discretize_xgb(Income, Assets, outcome = "Status") xgb_rec <- prep(xgb_rec, training = credit_data_tr) bake(xgb_rec, credit_data_te, Assets) #> # A tibble: 251 × 1 #> Assets #> <fct> #> 1 [3000,4000) #> 2 [3000,4000) #> 3 [9500, Inf] #> 4 [3000,4000) #> 5 [-Inf,2500) #> 6 [-Inf,2500) #> 7 [-Inf,2500) #> 8 [4000,4500) #> 9 [-Inf,2500) #> 10 [3000,4000) #> # ℹ 241 more rows