For a recipe with at least one preprocessing step that has been trained by
prep.recipe(), apply the computations to new data.
bake(object, ...)# S3 method for recipe
bake(object, newdata, ..., composition = "tibble")
A trained object such as a recipe() with at least
one preprocessing step.
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" 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 or sparse matrix that may have different
columns than the original columns in newdata.
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
newdata equal to the training set.