step_adasyn creates a specification of a recipe
step that generates synthetic positive instances using ADASYN algorithm.
step_adasyn( recipe, ..., role = NA, trained = FALSE, column = NULL, over_ratio = 1, neighbors = 5, skip = TRUE, seed = sample.int(10^5, 1), id = rand_id("adasyn") ) # S3 method for step_adasyn 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 neighbor 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 applied.
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.
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.
He, H., Bai, Y., Garcia, E. and Li, S. 2008. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference. pp.1322-1328.
#> #> <NA> stem other #> 0 9539 50316ds_rec <- recipe(Class ~ age + height, data = okc) %>% step_meanimpute(all_predictors()) %>% step_adasyn(Class) %>% prep() sort(table(bake(ds_rec, new_data = NULL)$Class, useNA = "always"))#> #> <NA> stem other #> 0 50316 50316# since `skip` defaults to TRUE, baking the step has no effect baked_okc <- bake(ds_rec, new_data = okc) table(baked_okc$Class, useNA = "always")#> #> stem other <NA> #> 9539 50316 0library(ggplot2) ggplot(circle_example, aes(x, y, color = class)) + geom_point() + labs(title = "Without ADASYN")recipe(class ~ ., data = circle_example) %>% step_adasyn(class) %>% prep() %>% bake(new_data = NULL) %>% ggplot(aes(x, y, color = class)) + geom_point() + labs(title = "With ADASYN")