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
One or more selector functions to choose which variables will be
returned by the function. See
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
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
prep() to avoid duplicating the same operations. With this
juice() can be used instead of
new_data equal to the training set.
Also, any steps with
skip = TRUE will not be applied to the
bake is invoked.
juice() will always have all
of the steps applied.