Impute nominal data using the most common valueSource:
step_impute_mode creates a specification of a
recipe step that will substitute missing values of nominal
variables by the training set mode of those variables.
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 named character vector of modes. This is
NULLuntil computed by
A data frame prototype to cast new data sets to. This is commonly a 0-row slice of the training set.
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 = TRUEas 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.
step_impute_mode estimates the variable modes
from the data used in the
training argument of
bake.recipe then applies the new
values to new data sets using these values. If the training set
data has more than one mode, one is selected at random.
recipes 0.1.16, this function name changed from
tidy() this step, a tibble with columns
terms (the selectors or variables selected) and
model (the mode
value) is returned.
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
data("credit_data", package = "modeldata") ## missing data per column vapply(credit_data, function(x) mean(is.na(x)), c(num = 0)) #> Status Seniority Home Time Age #> 0.0000000000 0.0000000000 0.0013471037 0.0000000000 0.0000000000 #> Marital Records Job Expenses Income #> 0.0002245173 0.0000000000 0.0004490346 0.0000000000 0.0855410867 #> Assets Debt Amount Price #> 0.0105523125 0.0040413112 0.0000000000 0.0000000000 set.seed(342) in_training <- sample(1:nrow(credit_data), 2000) credit_tr <- credit_data[in_training, ] credit_te <- credit_data[-in_training, ] missing_examples <- c(14, 394, 565) rec <- recipe(Price ~ ., data = credit_tr) impute_rec <- rec %>% step_impute_mode(Status, Home, Marital) imp_models <- prep(impute_rec, training = credit_tr) imputed_te <- bake(imp_models, new_data = credit_te, everything()) table(credit_te$Home, imputed_te$Home, useNA = "always") #> #> ignore other owner parents priv rent <NA> #> ignore 13 0 0 0 0 0 0 #> other 0 176 0 0 0 0 0 #> owner 0 0 1171 0 0 0 0 #> parents 0 0 0 436 0 0 0 #> priv 0 0 0 0 135 0 0 #> rent 0 0 0 0 0 519 0 #> <NA> 0 0 4 0 0 0 0 tidy(impute_rec, number = 1) #> # A tibble: 3 × 3 #> terms model id #> <chr> <chr> <chr> #> 1 Status NA impute_mode_Hlm2y #> 2 Home NA impute_mode_Hlm2y #> 3 Marital NA impute_mode_Hlm2y tidy(imp_models, number = 1) #> # A tibble: 3 × 3 #> terms model id #> <chr> <chr> <chr> #> 1 Status good impute_mode_Hlm2y #> 2 Home owner impute_mode_Hlm2y #> 3 Marital married impute_mode_Hlm2y