step_normalize | R Documentation |
step_normalize()
creates a specification of a recipe step that will
normalize numeric data to have a standard deviation of one and a mean of
zero.
step_normalize(
recipe,
...,
role = NA,
trained = FALSE,
means = NULL,
sds = NULL,
na_rm = TRUE,
skip = FALSE,
id = rand_id("normalize")
)
recipe |
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 |
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. |
means |
A named numeric vector of means. This is |
sds |
A named numeric vector of standard deviations This is |
na_rm |
A logical value indicating whether |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
A character string that is unique to this step to identify it. |
Centering data means that the average of a variable is subtracted
from the data. Scaling data means that the standard deviation of a variable
is divided out of the data. step_normalize
estimates the variable standard
deviations and means from the data used in the training
argument of
prep.recipe
. bake.recipe
then applies the scaling to new data sets using
these estimates.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble is returned with
columns terms
, statistic
, value
, and id
:
character, the selectors or variables selected
character, name of statistic ("mean"
or "sd"
)
numeric, value of the statistic
character, id of this step
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 normalization steps:
step_center()
,
step_range()
,
step_scale()
data(biomass, package = "modeldata")
biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]
rec <- recipe(
HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr
)
norm_trans <- rec %>%
step_normalize(carbon, hydrogen)
norm_obj <- prep(norm_trans, training = biomass_tr)
transformed_te <- bake(norm_obj, biomass_te)
biomass_te[1:10, names(transformed_te)]
transformed_te
tidy(norm_trans, number = 1)
tidy(norm_obj, number = 1)
# To keep the original variables in the output, use `step_mutate_at`:
norm_keep_orig <- rec %>%
step_mutate_at(all_numeric_predictors(), fn = list(orig = ~.)) %>%
step_normalize(-contains("orig"), -all_outcomes())
keep_orig_obj <- prep(norm_keep_orig, training = biomass_tr)
keep_orig_te <- bake(keep_orig_obj, biomass_te)
keep_orig_te
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