recipes: bake – R documentation

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bake

Apply a Trained Data Recipe

Description

For a recipe with at least one preprocessing operation that has been trained by prep.recipe(), apply the computations to new data.

Usage

bake(object, ...)

## S3 method for class 'recipe'
bake(object, new_data, ..., composition = "tibble")

Arguments

`object`	A trained object such as a `recipe()` with at least one preprocessing operation.
`...`	One or more selector functions to choose which variables will be returned by the function. See `selections()` for more details. If no selectors are given, the default is to use `everything()`.
`new_data`	A data frame or tibble for whom the preprocessing will be applied. If `NULL` is given to `new_data`, the pre-processed training data will be returned (assuming that `prep(retain = TRUE)` was used).
`composition`	Either "tibble", "matrix", "data.frame", or "dgCMatrix" for the format of the processed data set. Note that all computations during the baking process are done in a non-sparse format. Also, note that this argument should be called after any selectors and the selectors should only resolve to numeric columns (otherwise an error is thrown).

Details

bake() takes a trained recipe and applies the operations to a data set to create a design matrix.

If the data set is not too large, time can be saved by using the retain = TRUE option of prep(). This stores the processed version of the training set. With this option set, bake(object, new_data = NULL) will return it for free.

Also, any steps with skip = TRUE will not be applied to the data when bake() is invoked with a data set in new_data. bake(object, new_data = NULL) will always have all of the steps applied.

Value

A tibble, matrix, or sparse matrix that may have different columns than the original columns in new_data.

Author(s)

Max Kuhn

Examples

data(ames, package = "modeldata")

ames <- mutate(ames, Sale_Price = log10(Sale_Price))

ames_rec <-
  recipe(Sale_Price ~ ., data = ames[-(1:6), ]) %>%
  step_other(Neighborhood, threshold = 0.05) %>%
  step_dummy(all_nominal()) %>%
  step_interact(~ starts_with("Central_Air"):Year_Built) %>%
  step_ns(Longitude, Latitude, deg_free = 2) %>%
  step_zv(all_predictors()) %>%
  prep()

# return the training set (already embedded in ames_rec)
ames_train <- bake(ames_rec, new_data = NULL)

# apply processing to other data:
ames_new <- bake(ames_rec, new_data = head(ames))

recipes

Preprocessing Tools to Create Design Matrices

v0.1.16

MIT + file LICENSE

Authors

Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph]

Initial release