step_scale {recipes} | R Documentation |
step_scale
creates a specification of a recipe
step that will normalize numeric data to have a standard
deviation of one.
step_scale(recipe, ..., role = NA, trained = FALSE, sds = NULL, na.rm = TRUE) ## S3 method for class 'step_scale' tidy(x, ...)
recipe |
A recipe object. The step will be added to the sequence of operations for this recipe. |
... |
One or more selector functions to choose which
variables are affected by the step. See |
role |
Not used by this step since no new variables are created. |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
sds |
A named numeric vector of standard deviations This
is |
na.rm |
A logical value indicating whether |
x |
A |
Scaling data means that the standard deviation of a
variable is divided out of the data. step_scale
estimates
the variable standard deviations from the data used in the
training
argument of prep.recipe
.
bake.recipe
then applies the scaling to new data sets
using these standard deviations.
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
(the
selectors or variables selected) and value
(the
standard deviations).
data(biomass) biomass_tr <- biomass[biomass$dataset == "Training",] biomass_te <- biomass[biomass$dataset == "Testing",] rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur, data = biomass_tr) scaled_trans <- rec %>% step_scale(carbon, hydrogen) scaled_obj <- prep(scaled_trans, training = biomass_tr) transformed_te <- bake(scaled_obj, biomass_te) biomass_te[1:10, names(transformed_te)] transformed_te tidy(scaled_trans, number = 1) tidy(scaled_obj, number = 1)