Discretize numeric variables with XgBoost

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.

Usage

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")
)

Arguments

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 selections() for more details.
role: Defaults to "predictor".
trained: A logical to indicate if the quantities for preprocessing have been estimated.
outcome: A call to vars to specify which variable is used as the outcome to train XgBoost models in order to discretize explanatory variables.
sample_val: Share of data used for validation (with early stopping) of the learned splits (the rest is used for training). Defaults to 0.20.
learn_rate: The rate at which the boosting algorithm adapts from iteration-to-iteration. Corresponds to eta in the xgboost package. Defaults to 0.3.
num_breaks: The maximum number of discrete bins to bucket continuous features. Corresponds to max_bin in the xgboost package. Defaults to 10.
tree_depth: The maximum depth of the tree (i.e. number of splits). Corresponds to max_depth in the xgboost package. Defaults to 1.
min_n: The minimum number of instances needed to be in each node. Corresponds to min_child_weight in the xgboost package. Defaults to 5.
rules: The splitting rules of the best XgBoost tree to retain for each variable.
skip: 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 = TRUE as it may affect the computations for subsequent operations
id: A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

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 data.

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 modifying the tree_depth or 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.

Tidying

When you tidy() this step, a tibble with columns terms (the columns that is selected), values is returned.

Tuning Parameters

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)

Case weights

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.

Examples

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