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, strings_as_factors = TRUE, ...)
an object
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
training.
A logical that controls whether progress is reported as operations are executed.
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.
A logical: should character columns be converted to
factors? This affects the preprocessed training set (when
retain = TRUE) as well as the results of bake.recipe.
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.
Given a data set, this function estimates the required quantities and statistics required by any operations.
prep() returns an updated recipe with the estimates.
Note that missing data handling 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.