For a recipe with at least one preprocessing operations that has been trained by
prep.recipe(), apply the computations to new data.
bake(object, ...)# S3 method for recipe
bake(object, new_data = NULL, ..., composition = "tibble")
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().
A data frame or tibble for whom the preprocessing will be applied.
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).
A tibble, matrix, or sparse matrix that may have different
columns than the original columns in new_data.
bake() takes a trained recipe and applies the
operations to a data set to create a design matrix.
If the original data used to train the data are to be
processed, time can be saved by using the retain = TRUE option
of prep() to avoid duplicating the same operations. With this
option set, juice() can be used instead of bake with
new_data equal to the training set.
Also, any steps with skip = TRUE will not be applied to the
data when bake is invoked. juice() will always have all
of the steps applied.