Estimate a preprocessing recipe

For a recipe with at least one preprocessing operation, estimate the required parameters from a training set that can be later applied to other data sets.

Usage

prep(x, ...)

# S3 method for class 'recipe'
prep(
  x,
  training = NULL,
  fresh = FALSE,
  verbose = FALSE,
  retain = TRUE,
  log_changes = FALSE,
  strings_as_factors = TRUE,
  ...
)

Arguments

x: an recipe() object.
...: further arguments passed to or from other methods (not currently used).
training: A data frame, tibble, or sparse matrix from the Matrix package, that will be used to estimate parameters for preprocessing. See sparse_data for more information about use of sparse data.
fresh: A logical indicating whether already trained operation should be re-trained. If TRUE, you should pass in a data set to the argument training.
verbose: A logical that controls whether progress is reported as operations are executed.
retain: A logical: should the preprocessed training set be saved into the template slot of the recipe after training? This is a good idea if you want to add more steps later but want to avoid re-training the existing steps. Also, it is advisable to use retain = TRUE if any steps use the option skip = FALSE. Note that this can make the final recipe size large. When verbose = TRUE, a message is written with the approximate object size in memory but may be an underestimate since it does not take environments into account.
log_changes: A logical for printing a summary for each step regarding which (if any) columns were added or removed during training.
strings_as_factors: A logical: should character columns that have role "predictor" or "outcome" be converted to factors? This affects the preprocessed training set (when retain = TRUE) as well as the results of bake().

Value

A recipe whose step objects have been updated with the required quantities (e.g. parameter estimates, model objects, etc). Also, the term_info object is likely to be modified as the operations are executed.

Details

Given a data set, this function estimates the required quantities and statistics needed by any operations. prep() returns an updated recipe with the estimates. If you are using a recipe as a preprocessor for modeling, we highly recommend that you use a workflow() instead of manually estimating a recipe (see the example in recipe()).

Note that missing data is handled in the steps; there is no global na.rm option at the recipe level or in prep().

Also, if a recipe has been trained using prep() and then steps are added, prep() will only update the new operations. If fresh = TRUE, all of the operations will be (re)estimated.

As the steps are executed, the training set is updated. For example, if the first step is to center the data and the second is to scale the data, the step for scaling is given the centered data.

Examples

data(ames, package = "modeldata")

library(dplyr)

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

ames_rec <-
  recipe(
    Sale_Price ~ Longitude + Latitude + Neighborhood + Year_Built + Central_Air,
    data = ames
  ) %>%
  step_other(Neighborhood, threshold = 0.05) %>%
  step_dummy(all_nominal()) %>%
  step_interact(~ starts_with("Central_Air"):Year_Built) %>%
  step_ns(Longitude, Latitude, deg_free = 5)

prep(ames_rec, verbose = TRUE)
#> oper 1 step other [training] 
#> oper 2 step dummy [training] 
#> oper 3 step interact [training] 
#> oper 4 step ns [training] 
#> The retained training set is ~ 0.48 Mb  in memory.
#> 
#> 
#> ── Recipe ────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 5
#> 
#> ── Training information 
#> Training data contained 2930 data points and no incomplete rows.
#> 
#> ── Operations 
#> • Collapsing factor levels for: Neighborhood | Trained
#> • Dummy variables from: Neighborhood and Central_Air | Trained
#> • Interactions with: Central_Air_Y:Year_Built | Trained
#> • Natural splines on: Longitude and Latitude | Trained

prep(ames_rec, log_changes = TRUE)
#> step_other (other_JgwxH): same number of columns
#> 
#> step_dummy (dummy_Fe2kG): 
#>  new (9): Neighborhood_College_Creek, Neighborhood_Old_Town, ...
#>  removed (2): Neighborhood, Central_Air
#> 
#> step_interact (interact_LGGmr): 
#>  new (1): Central_Air_Y_x_Year_Built
#> 
#> step_ns (ns_lWKqm): 
#>  new (10): Longitude_ns_1, Longitude_ns_2, Longitude_ns_3, ...
#>  removed (2): Longitude, Latitude
#> 
#> 
#> ── Recipe ────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> outcome:   1
#> predictor: 5
#> 
#> ── Training information 
#> Training data contained 2930 data points and no incomplete rows.
#> 
#> ── Operations 
#> • Collapsing factor levels for: Neighborhood | Trained
#> • Dummy variables from: Neighborhood and Central_Air | Trained
#> • Interactions with: Central_Air_Y:Year_Built | Trained
#> • Natural splines on: Longitude and Latitude | Trained