step_filter_missing() creates a specification of a recipe step that will
potentially remove variables that have too many missing values.
step_filter_missing(
recipe,
...,
role = NA,
trained = FALSE,
threshold = 0.1,
removals = NULL,
skip = FALSE,
id = rand_id("filter_missing")
)A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose variables
for this step. See selections() for more details.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A value for the threshold of missing values in column. The step will remove the columns where the proportion of missing values exceeds the threshold.
A character string that contains the names of
columns that should be removed. These values are not determined
until prep() is called.
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.
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 any existing operations.
This step can potentially remove columns from the data set. This may cause issues for subsequent steps in your recipe if the missing columns are specifically referenced by name. To avoid this, see the advice in the Tips for saving recipes and filtering columns section of selections.
This step will remove variables if the proportion of missing
values exceeds the threshold.
All variables with missing values will be removed for threshold = 0.
When you tidy() this step, a tibble is returned with
columns terms and id:
character, the selectors or variables selected
character, id of this step
This step has 1 tuning parameters:
threshold: Threshold (type: double, default: 0.1)
This step performs an unsupervised operation that can utilize case weights.
As a result, case weights are only used with frequency weights. For more
information, see the documentation in case_weights and the examples on
tidymodels.org.
Other variable filter steps:
step_corr(),
step_lincomb(),
step_nzv(),
step_rm(),
step_select(),
step_zv()
data(credit_data, package = "modeldata")
rec <- recipe(Status ~ ., data = credit_data) %>%
step_filter_missing(all_predictors(), threshold = 0)
filter_obj <- prep(rec)
filtered_te <- bake(filter_obj, new_data = NULL)
tidy(rec, number = 1)
#> # A tibble: 1 × 2
#> terms id
#> <chr> <chr>
#> 1 all_predictors() filter_missing_IYaDd
tidy(filter_obj, number = 1)
#> # A tibble: 6 × 2
#> terms id
#> <chr> <chr>
#> 1 Home filter_missing_IYaDd
#> 2 Marital filter_missing_IYaDd
#> 3 Job filter_missing_IYaDd
#> 4 Income filter_missing_IYaDd
#> 5 Assets filter_missing_IYaDd
#> 6 Debt filter_missing_IYaDd