Workflows encompasses the three main stages of the modeling process: pre-processing of data, model fitting, and post-processing of results. This page enumerates the possible operations for each stage that have been implemented to date.
There are three options for pre-processing but you can only use one of them in a single workflow:
A standard model
formula via add_formula().
A tidyselect interface via add_variables() that strictly
preserves the class of your columns.
A recipe object via add_recipe().
parsnip model specifications are the only option here,
specified via add_model().
When using a preprocessor, you may need an additional formula for
special model terms (e.g. for mixed models or generalized linear
models). In these cases, specify that formula using
add_model()’s formula argument, which will be
passed to the underlying model when fit() is called.
tailor post-processors are the only option here,
specified via add_tailor(). Some examples of
post-processing model predictions could include adding a probability
threshold for two-class problems, calibration of probability estimates,
truncating the possible range of predictions, and so on.
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