step_smogn: Apply SMOGN Algorithm

In themis: Extra Recipes Steps for Dealing with Unbalanced Data

Apply SMOGN Algorithm

Description

step_smogn() creates a specification of a recipe step that generates new examples for imbalanced regression problems using SMOGN.

Usage

step_smogn(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  threshold = 0.5,
  relevance = NULL,
  neighbors = 5,
  perturbation = 0.02,
  distance = "euclidean",
  indicator_column = NULL,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  id = rand_id("smogn")
)

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 variable is used to sample the data. See recipes::selections for more details. The selection should result in single numeric variable. For the tidy method, these are not currently used.
role	Not used by this step since no new variables are created.
trained	A logical to indicate if the quantities for preprocessing have been estimated.
column	A character string of the variable name that will be populated (eventually) by the ... selectors.
threshold	A number between 0 and 1. Outcome values with a relevance at or above this value are treated as rare and over-sampled. Defaults to 0.5.
relevance	A matrix of relevance control points, or NULL (default). When NULL, relevance is derived automatically from the boxplot extremes of the outcome. When supplied, the first column gives outcome values and the second column their relevance in ⁠[0, 1]⁠.
neighbors	An integer. Number of nearest neighbor that are used to generate the new examples of the rare values.
perturbation	A number. The magnitude of the Gaussian noise added when generating synthetic examples in unsafe regions. Defaults to 0.02.
distance	A character string specifying the distance metric used for nearest neighbor calculations, defaulting to "euclidean". The available metrics fall into three groups. "euclidean", "cosine", and "mahalanobis" use approximate nearest neighbors via the RANN package and scale well to large datasets. "squared_chord", "matusita", "hellinger", and "bhattacharyya" are probability-divergence measures that treat each row as a distribution over the predictors, so they require non-negative values. "hellinger" and "bhattacharyya" further require each row to sum to 1. All four also use the RANN package and scale well to large datasets. "manhattan", "chebyshev", "canberra", "soergel", "lorentzian", "jeffreys", "topsoe", "jensen-shannon", "jensen_difference", "taneja", and "kumar-johnson" compute an exact all-pairs distance matrix. This takes time and memory proportional to the square of the number of observations in a class, so these are best suited to smaller datasets. Everything from "canberra" onwards is a probability divergence requiring non-negative values, is provided by the philentropy package (which must be installed separately), and in the case of "jeffreys", "taneja", and "kumar-johnson" requires strictly positive values, since those divide by individual predictor values. The probability divergences are meaningful for compositional predictors such as proportions or counts normalized per observation, and are generally not appropriate for standardized predictors.
indicator_column	A single string or NULL (the default). If a string is given, a logical column with that name is added to the output, marking rows added by the step (TRUE) vs rows from the original data (FALSE).
skip	A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when 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.
seed	An integer that will be used as the seed when applied.
id	A character string that is unique to this step to identify it.

Details

SMOGN is a pre-processing approach for imbalanced regression. A relevance function assigns each outcome value a relevance score, and values with a relevance at or above threshold are treated as rare. The data is split into contiguous bins of rare and common outcome values. Common bins are under-sampled and rare bins are over-sampled toward a balanced size. New rare examples are generated either by interpolating between an example and a nearby neighbor (when they are close enough to be considered safe) or by perturbing the example with Gaussian noise (when they are not), where the amount of noise is controlled by perturbation.

By default relevance is derived automatically from the boxplot extremes of the outcome, giving the median a relevance of 0 and the extreme values a relevance of 1. A matrix of relevance control points can instead be supplied through relevance, with the first column giving outcome values and the second column their relevance.

All columns in the data are sampled and returned by recipes::juice() and recipes::bake().

All columns used in this step must be numeric with no missing data.

When used in modeling, users should strongly consider using the option skip = TRUE so that the extra sampling is not conducted outside of the training set.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms which is the variable used to sample.

Tidying

When you tidy() this step, a tibble is returned with columns terms and id:

terms

character, the selectors or variables selected

id

character, id of this step

Tuning Parameters

This step has 2 tuning parameters:

• neighbors: # Nearest Neighbors (type: integer, default: 5)

• threshold: Threshold (type: double, default: 0.5)

Case weights

The underlying operation does not allow for case weights. Supplying data with a case weights column to this step results in an error.

References

Branco, P., Torgo, L., and Ribeiro, R. P. (2017). SMOGN: a pre-processing approach for imbalanced regression. Proceedings of Machine Learning Research, 74:36-50.

See Also

smogn() for direct implementation

Other Steps for over-sampling: step_adasyn(), step_bsmote(), step_kmeans_smote(), step_rose(), step_smote(), step_smoten(), step_smotenc(), step_svmsmote(), step_upsample()

Examples

library(recipes)
library(ggplot2)

ggplot(circle_example, aes(x)) +
  geom_histogram(bins = 30) +
  labs(title = "Without SMOGN")

recipe(y ~ x, data = circle_example) |>
  step_smogn(y) |>
  prep() |>
  bake(new_data = NULL) |>
  ggplot(aes(y)) +
  geom_histogram(bins = 30) +
  labs(title = "With SMOGN")

themis documentation built on Aug. 2, 2026, 9:07 a.m.