step_downsample creates a specification of a recipe
step that will remove rows of a data set to make the occurrence
of levels in a specific factor level equal.
step_downsample( recipe, ..., under_ratio = 1, ratio = NA, role = NA, trained = FALSE, column = NULL, target = NA, skip = TRUE, seed = sample.int(10^5, 1), id = rand_id("downsample") ) # S3 method for step_downsample 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
A numeric value for the ratio of the minority-to-majority frequencies. The default value (1) means that all other levels are sampled down to have the same frequency as the least occurring level. A value of 2 would mean that the majority levels will have (at most) (approximately) twice as many rows than the minority level.
Depracated argument; same as
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
An integer that will be used to subsample. This
should not be set by the user and will be populated by
A logical. Should the step be skipped when the
recipe is baked by
An integer that will be used as the seed when downsampling.
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.
Down-sampling is intended to be performed on the training set
alone. For this reason, the default is
skip = TRUE. It is
advisable to use
prep(recipe, retain = TRUE) when preparing
the recipe; in this way
juice() can be used to obtain the
down-sampled version of the data.
If there are missing values in the factor variable that is used to define the sampling, missing data are selected at random in the same way that the other factor levels are sampled. Missing values are not used to determine the amount of data in the minority level
For any data with factor levels occurring with the same frequency as the minority level, all data will be retained.
Keep in mind that the location of down-sampling in the step may have effects. For example, if centering and scaling, it is not clear whether those operations should be conducted before or after rows are removed.
#> #> halal kosher strictly halal strictly kosher #> 11 11 18 18 #> mostly halal mostly kosher vegan strictly vegan #> 48 86 136 227 #> other mostly vegan strictly other vegetarian #> 331 335 450 665 #> strictly vegetarian mostly other mostly vegetarian strictly anything #> 874 1004 3438 5107 #> anything mostly anything <NA> #> 6174 16562 24360ds_rec <- recipe( ~ ., data = okc) %>% step_downsample(diet) %>% prep(training = okc, retain = TRUE) sort(table(juice(ds_rec)$diet, useNA = "always"))#> #> anything halal kosher mostly anything #> 11 11 11 11 #> mostly halal mostly kosher mostly other mostly vegan #> 11 11 11 11 #> mostly vegetarian other strictly anything strictly halal #> 11 11 11 11 #> strictly kosher strictly other strictly vegan strictly vegetarian #> 11 11 11 11 #> vegan vegetarian <NA> #> 11 11 11# since `skip` defaults to TRUE, baking the step has no effect baked_okc <- bake(ds_rec, new_data = okc) table(baked_okc$diet, useNA = "always")#> #> anything halal kosher mostly anything #> 6174 11 11 16562 #> mostly halal mostly kosher mostly other mostly vegan #> 48 86 1004 335 #> mostly vegetarian other strictly anything strictly halal #> 3438 331 5107 18 #> strictly kosher strictly other strictly vegan strictly vegetarian #> 18 450 227 874 #> vegan vegetarian <NA> #> 136 665 24360