step_smote creates a specification of a recipe
step that generate new examples of the minority class using nearest
neighbors of these cases.
step_smote( recipe, ..., role = NA, trained = FALSE, column = NULL, over_ratio = 1, neighbors = 5, skip = TRUE, seed = sample.int(10^5, 1), id = rand_id("smote") ) # S3 method for step_smote tidy(x, ...)
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
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A character string of the variable name that will
be populated (eventually) by the
A numeric value for the ratio of the majority-to-minority frequencies. The default value (1) means that all other levels are sampled up to have the same frequency as the most occurring level. A value of 0.5 would mean that the minority levels will have (at most) (approximately) half as many rows than the majority level.
An integer. Number of nearest neighbours that are used to generate the new examples of the minority class.
A logical. Should the step be skipped when the
recipe is baked by
An integer that will be used as the seed when smote-ing.
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 existing steps (if any). For the
tidy method, a tibble with columns
terms which is
the variable used to sample.
neighbors controls the way the new examples are created.
For each currently existing minority class example X new examples will be
created (this is controlled by the parameter
over_ratio as mentioned
above). These examples will be generated by using the information from the
neighbors nearest neighbours of each example of the minority class.
neighbors controls how many of these neighbours are used.
All columns used in this step must be numeric with no missing data.
When used in modeling, users should strongly consider using the
skip = TRUE so that the extra sampling is not
conducted outside of the training set.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321-357.
library(recipes) library(modeldata) data(credit_data) sort(table(credit_data$Status, useNA = "always"))#> #> <NA> bad good #> 0 1254 3200ds_rec <- recipe(Status ~ Age + Income + Assets, data = credit_data) %>% step_meanimpute(all_predictors()) %>% step_smote(Status) %>% prep() sort(table(juice(ds_rec)$Status, useNA = "always"))#> #> <NA> bad good #> 0 3200 3200# since `skip` defaults to TRUE, baking the step has no effect baked_okc <- bake(ds_rec, new_data = credit_data) table(baked_okc$Status, useNA = "always")#> #> bad good <NA> #> 1254 3200 0ds_rec2 <- recipe(Status ~ Age + Income + Assets, data = credit_data) %>% step_meanimpute(all_predictors()) %>% step_smote(Status, over_ratio = 0.2) %>% prep() table(juice(ds_rec2)$Status, useNA = "always")#> #> bad good <NA> #> 1254 3200 0library(ggplot2) ggplot(circle_example, aes(x, y, color = class)) + geom_point() + labs(title = "Without SMOTE")recipe(class ~ ., data = circle_example) %>% step_smote(class) %>% prep() %>% juice() %>% ggplot(aes(x, y, color = class)) + geom_point() + labs(title = "With SMOTE")