Skip to content

step_poly_bernstein creates a specification of a recipe step that creates Bernstein polynomial features.

Usage

step_poly_bernstein(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  degree = 10,
  complete_set = FALSE,
  options = NULL,
  keep_original_cols = FALSE,
  results = NULL,
  skip = FALSE,
  id = rand_id("poly_bernstein")
)

Arguments

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 selections() for more details.

role

For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

degree

The degrees of the polynomial. As the degrees for a polynomial increase, more flexible and complex curves can be generated.

complete_set

If TRUE, the complete basis matrix will be returned. Otherwise, the first basis will be excluded from the output. This maps to the intercept argument of the corresponding function from the splines2 package and has the same default value.

options

A list of options for splines2::bernsteinPoly() which should not include x or degree.

keep_original_cols

A logical to keep the original variables in the output. Defaults to FALSE.

results

A list of objects created once the step has been trained.

skip

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.

id

A character string that is unique to this step to identify it.

Value

An object with classes "step_poly_bernstein" and "step".

Details

Polynomial transformations take a numeric column and create multiple features that, when used in a model, can estimate nonlinear trends between the column and some outcome. The degrees of freedom determines how many new features are added to the data.

If the spline expansion fails for a selected column, the step will remove that column's results (but will retain the original data). Use the tidy() method to determine which columns were used.

Tidying

When you tidy() this step, a tibble with column terms (the columns that will be affected) is returned.

Case weights

The underlying operation does not allow for case weights.

Examples

library(tidyr)
library(dplyr)

library(ggplot2)
data(ames, package = "modeldata")

spline_rec <- recipe(Sale_Price ~ Longitude, data = ames) %>%
  step_poly_bernstein(Longitude, degree = 6, keep_original_cols = TRUE) %>%
  prep()

tidy(spline_rec, number = 1)
#> # A tibble: 1 × 2
#>   terms     id                  
#>   <chr>     <chr>               
#> 1 Longitude poly_bernstein_EVN95

# Show where each feature is active
spline_rec %>%
  bake(new_data =  NULL,-Sale_Price) %>%
  pivot_longer(c(starts_with("Longitude_")), names_to = "feature", values_to = "value") %>%
  mutate(feature = gsub("Longitude_", "feature ", feature)) %>%
  filter(value > 0) %>%
  ggplot(aes(x = Longitude, y = value)) +
  geom_line() +
  facet_wrap(~ feature)