Estimate a preprocessing recipeSource:
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.
prep(x, ...) # S3 method for recipe prep( x, training = NULL, fresh = FALSE, verbose = FALSE, retain = TRUE, log_changes = FALSE, strings_as_factors = TRUE, ... )
further arguments passed to or from other methods (not currently used).
A data frame or tibble that will be used to estimate parameters for preprocessing.
A logical indicating whether already trained operation should be re-trained. If
TRUE, you should pass in a data set to the argument
A logical that controls whether progress is reported as operations are executed.
A logical: should the preprocessed training set be saved into the
templateslot 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 = TRUEif 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.
A logical for printing a summary for each step regarding which (if any) columns were added or removed during training.
A logical: should character columns be converted to factors? This affects the preprocessed training set (when
retain = TRUE) as well as the results of
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
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
Note that missing data is handled in the steps; there is no global
na.rm option at the recipe level or in
Also, if a recipe has been trained using
prep() and then steps
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.
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 ~ 0.49 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, Central_Air | Trained #> • Interactions with: Central_Air_Y:Year_Built | Trained #> • Natural splines on: Longitude, Latitude | Trained prep(ames_rec, log_changes = TRUE) #> step_other (other_A3lud): same number of columns #> #> step_dummy (dummy_ly8qZ): #> new (9): Neighborhood_College_Creek, Neighborhood_Old_Town, ... #> removed (2): Neighborhood, Central_Air #> #> step_interact (interact_Z0B89): #> new (1): Central_Air_Y_x_Year_Built #> #> step_ns (ns_Iiisv): #> 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, Central_Air | Trained #> • Interactions with: Central_Air_Y:Year_Built | Trained #> • Natural splines on: Longitude, Latitude | Trained