Creates a new step_obwoe object. This is an internal function and should not be called directly by users.
step_obwoe_new(
terms,
role,
trained,
outcome,
algorithm,
min_bins,
max_bins,
bin_cutoff,
output,
suffix_woe,
suffix_bin,
na_woe,
control,
binning_results,
skip,
id
)A step_obwoe object.
A list of quosures specifying the variables to transform.
For variables created by this step, what role should they have?
Default is "predictor".
A logical indicating whether the step has been trained (fitted). This should not be set manually.
A character string specifying the name of the binary or
multinomial response variable. This argument is required as all
binning algorithms are supervised. The outcome must exist in the training
data provided to prep(). The outcome should be encoded as a factor
(standard for tidymodels classification) or as integers 0/1 for binary,
0/1/2/... for multinomial.
Character string specifying the binning algorithm to use.
Use "auto" (default) for automatic selection based on target type:
"jedi" for binary targets, "jedi_mwoe" for multinomial.
Available algorithms are organized by supported feature types:
Universal (numerical and categorical):
"auto", "jedi", "jedi_mwoe", "cm", "dp",
"dmiv", "fetb", "mob", "sketch", "udt"
Numerical only:
"bb", "ewb", "fast_mdlp", "ir", "kmb",
"ldb", "lpdb", "mblp", "mdlp", "mrblp",
"oslp", "ubsd"
Categorical only:
"gmb", "ivb", "mba", "milp", "sab",
"sblp", "swb"
This parameter is tunable with tune().
Integer specifying the minimum number of bins to create.
Must be at least 2. Default is 2. This parameter is tunable with
tune().
Integer specifying the maximum number of bins to create.
Must be greater than or equal to min_bins. Default is 10. This
parameter is tunable with tune().
Numeric value between 0 and 1 (exclusive) specifying the
minimum proportion of total observations that each bin must contain. Bins
with fewer observations are merged with adjacent bins. This serves as a
regularization mechanism to prevent overfitting and ensure statistical
stability of WoE estimates. Default is 0.05 (5%). This parameter is
tunable with tune().
Character string specifying the transformation output format:
"woe"Replaces the original variable with WoE values (default). This is the standard choice for logistic regression scorecards.
"bin"Replaces the original variable with bin labels (character). Useful for tree-based models or exploratory analysis.
"both"Keeps the original column and adds two new columns
with suffixes _woe and _bin. Useful for model comparison
or audit trails.
Character string suffix appended to create WoE column names
when output = "both". Default is "_woe".
Character string suffix appended to create bin column names
when output = "both". Default is "_bin".
Numeric value to assign to observations that cannot be mapped
to a bin during bake(). This includes missing values (NA) and
unseen categories not present in the training data. Default is 0, which
represents neutral evidence (neither good nor bad).
A named list of additional control parameters passed to
control.obwoe. These provide fine-grained control over
algorithm behavior such as convergence thresholds and maximum pre-bins.
Parameters specified directly in step_obwoe() (e.g.,
bin_cutoff) take precedence over values in this list.
Internal storage for fitted binning models after
prep(). Do not set manually.
Logical. Should this step be skipped when bake() is
called on new data? Default is FALSE. Setting to TRUE is
rarely needed for WoE transformations but may be useful in specialized
workflows.
A unique character string to identify this step. If not provided, a random identifier is generated.