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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.