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,
factor = 1,
na_rm = TRUE,
skip = FALSE,
id = rand_id("scale")
)| 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. |
| sds | A named numeric vector of standard deviations. This
is |
| factor | A numeric value of either 1 or 2 that scales the
numeric inputs by one or two standard deviations. By dividing
by two standard deviations, the coefficients attached to
continuous predictors can be interpreted the same way as with
binary inputs. Defaults to |
| 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. |
An updated version of recipe with the new step added to the
sequence of any existing operations.
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.
When you tidy() this step, a tibble with columns terms (the
selectors or variables selected) and value (the
standard deviations) is returned.
Gelman, A. (2007) "Scaling regression inputs by dividing by two standard deviations." Unpublished. Source: http://www.stat.columbia.edu/~gelman/research/unpublished/standardizing.pdf.
Other normalization steps:
step_center(),
step_normalize(),
step_range()
library(modeldata)
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)]
#> carbon hydrogen oxygen nitrogen sulfur HHV
#> 15 46.35 5.67 47.20 0.30 0.22 18.275
#> 20 43.25 5.50 48.06 2.85 0.34 17.560
#> 26 42.70 5.50 49.10 2.40 0.30 17.173
#> 31 46.40 6.10 37.30 1.80 0.50 18.851
#> 36 48.76 6.32 42.77 0.20 0.00 20.547
#> 41 44.30 5.50 41.70 0.70 0.20 18.467
#> 46 38.94 5.23 54.13 1.19 0.51 15.095
#> 51 42.10 4.66 33.80 0.95 0.20 16.240
#> 55 29.20 4.40 31.10 0.14 4.90 11.147
#> 65 27.80 3.77 23.69 4.63 1.05 10.750
transformed_te
#> # A tibble: 80 × 6
#> carbon hydrogen oxygen nitrogen sulfur HHV
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 4.45 4.74 47.2 0.3 0.22 18.3
#> 2 4.16 4.60 48.1 2.85 0.34 17.6
#> 3 4.10 4.60 49.1 2.4 0.3 17.2
#> 4 4.46 5.10 37.3 1.8 0.5 18.9
#> 5 4.68 5.28 42.8 0.2 0 20.5
#> 6 4.26 4.60 41.7 0.7 0.2 18.5
#> 7 3.74 4.37 54.1 1.19 0.51 15.1
#> 8 4.04 3.89 33.8 0.95 0.2 16.2
#> 9 2.81 3.68 31.1 0.14 4.9 11.1
#> 10 2.67 3.15 23.7 4.63 1.05 10.8
#> # … with 70 more rows
tidy(scaled_trans, number = 1)
#> # A tibble: 2 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 carbon NA scale_uY1pe
#> 2 hydrogen NA scale_uY1pe
tidy(scaled_obj, number = 1)
#> # A tibble: 2 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 carbon 10.4 scale_uY1pe
#> 2 hydrogen 1.20 scale_uY1pe